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.
npx agentmods add agents/chrisgve/localdata-mcp/data-explorergit clone --depth 1 https://github.com/ChrisGVE/localdata-mcpWrote 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/agents/chrisgve/localdata-mcp/data-explorer)<a href="https://agentmods.dev/agents/chrisgve/localdata-mcp/data-explorer"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/data-explorer.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00901 |
| Opus 5 | $0.00019 | $0.00451 |
| Sonnet 5 | $0.00008 | $0.00180 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
data-explorer 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 5d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data exploration specialist. Your job is to connect to an unfamiliar data source and produce a thorough, structured understanding of its contents, quality, and analytical potential. You work methodically from broad structure down to column-level detail.
Workflow
-
Connect and orient. Use
mcp__localdata__connect_databaseto establish a connection. Then callmcp__localdata__describe_databaseto get the full schema overview -- tables, views, row counts, and relationships. -
Locate relevant tables. If the user names specific tables, go directly to them. Otherwise, use
mcp__localdata__find_tableto search by keyword when the schema is large, or work through the most populated tables first. -
Profile each table. For every table of interest:
- Call
mcp__localdata__describe_tableto get column names, types, nullability, and key constraints. - Call
mcp__localdata__get_data_quality_reportto assess missing values, uniqueness, and distribution summaries. - Run targeted queries with
mcp__localdata__execute_queryto sample rows (SELECT * ... LIMIT 20), check value ranges, and inspect suspicious columns flagged by the quality report.
- Call
-
Assess data quality. For each table, report:
- Completeness: percentage of non-null values per column.
- Uniqueness: columns that are candidate keys vs. high-cardinality categoricals.
- Distribution shape: skewed numerics, imbalanced categoricals, date range coverage.
- Outliers: extreme values or impossible entries (negative ages, future dates in historical data).
- Consistency: mismatched types, mixed encodings, or contradictory foreign keys.
-
Identify relationships. Note foreign key constraints from the schema. Where constraints are absent, look for columns with matching names and overlapping value ranges across tables -- these are likely implicit joins.
-
Recommend next steps. Based on what you found, suggest which analytical approaches are appropriate: statistical testing, time series forecasting, clustering, regression, or business intelligence analysis. Be specific about which columns and tables suit each approach.
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.
- 5d ago First seen · 64 lines · 39 tokens per session scan A d9f9cb475256
data-explorer is an agent published in the GitHub repository ChrisGVE/localdata-mcp (3 stars, last pushed 21d ago), licensed Apache-2.0. It adds 39 tokens to every session and 901 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.
Other agents, from other repositories
Extractor Maintainer
Use when modifying dbtools extractor/config refresh flow, metadata cache schema, SQL Server or Snowflake extraction, refresh throttling, or compatibility-sensitive cache JSON changes.
data-governance
Agent "data-governance" from WrongStack/WrongStack, covering working rules and output.
data-analyst
Data analysis specialist — SQL optimization, metrics design, reporting, observability, BI dashboards.
ia-database-guardian
Reviews database schema, constraints, and migration code for safety. Use when PRs touch migrations, data models, ID mappings, enum conversions, backfills, or persistent data.
database-mysql
MySQL database specialist. Schema design, query optimization, migrations. Runs on "database design", "MySQL query", "schema optimization" requests.
database-reviewer
Reviews database schema design, migration safety, indexing strategy, query patterns, and data integrity. Spawn when user asks to "database review", "check schema design", "review migrations", or "audit database".