Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ololand-ai/ololand-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/ololand-ai/ololand-plugins/deal-search)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/deal-search"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/deal-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/deal-search"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/deal-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00881 |
| Opus 5 | $0.00022 | $0.00441 |
| Sonnet 5 | $0.00009 | $0.00176 |
| Haiku 4.5 | $0.00004 | $0.00088 |
Grade A, and why
deal-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deal Document Search
Search across all documents in a deal's data room using hybrid search (dense + sparse + rerank).
Usage
/deal-search <deal_id> <query>
Arguments
deal_id(required) — The deal to search within.query(required) — Natural language search query.
Execution
- Call
search_deal_documentswith the deal_id and query. - Present results with:
- Document name and page number
- Relevant excerpt (highlighted match)
- Relevance score
- If the query implies a financial figure, cross-reference with
get_financial_snapshotto check consistency.
Examples
/deal-search deal123 customer concentration risk/deal-search deal123 revenue recognition policy/deal-search deal123 change of control provisions/deal-search deal123 EBITDA adjustments and add-backs
Document navigation primitives
Semantic search (above) answers "what's relevant to this meaning." These
tools answer "I know (or can find) exactly where this is" — literal path- and
structure-based access to the same data-room text, complementing
search_deal_documents (semantic) and grep_filing (SEC-filing chunks).
- List —
mcp__ololand__list_deal_files(deal_id)returns every document's data-room path,file_id, category, size, and modified time. Call this beforeread_deal_file/grep_deal_filesto see what exists. - Read —
mcp__ololand__read_deal_file(path, deal_id, offset, limit)reads a known document's full text by data-room path, paginated by line (frontmatter stripped,file_idcarried for citation). - Grep —
mcp__ololand__grep_deal_files(pattern, deal_id, glob, case_sensitive, max_results)is literal/regex pattern matching over the markdown mirror — use this instead ofsearch_deal_documentswhen the user needs an exact string or regex match, not a meaning-based hit.globscopes to a folder/type, e.g.financials/*. - Read a structural section —
mcp__ololand__read_section(deal_id, file_id, section)reads a named section end-to-end (e.g. "Item 7. MD&A", "Note 11", "Risk Factors", "Consolidated Balance Sheet"). Section matching is case-insensitive substring; requiresfile_idfromlist_deal_files. Documents without structural sections return the full document instead (matched_field="document"); a near-miss on a structured document returnsavailable_sectionsto retry against. - Read a structured table —
mcp__ololand__read_table(deal_id, file_id, table_label)returns a document-graphTableNodeas typed rows × columns with cell-level span IDs — not chunked text, so the row/column structure survives. Returns an error if the graph has no matching table; there is no chunk-based fallback for this one, deliberately, since chunking would destroy exactly the structure this tool exists to preserve. - Read a financial-statement note —
mcp__ololand__read_note(deal_id, file_id, note_number)reads a numbered note in full, including its outgoingcross_references— follow a Note 11 → Note 8 → Note 18 chain with repeated calls, orlist_cross_referencesfor the whole graph at once.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 75 lines · 44 tokens per session scan A bf6c8113d45f
deal-search is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 4d ago), licensed Apache-2.0. It adds 44 tokens to every session and 881 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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