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 rules/tecton-ai/tecton-mcp/tectongit clone --depth 1 https://github.com/tecton-ai/tecton-mcpWhat 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 | $0.00011 | $0.01589 |
| Opus 5 | $0.00005 | $0.00794 |
| Sonnet 5 | $0.00002 | $0.00318 |
| Haiku 4.5 | $0.00001 | $0.00159 |
Grade A, and why
tecton 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 2d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rules to follow when you create a feature
- You must break down the implementation of features into stages:
- First, fetch and review relevant rules
- Then, search for Tecton examples and the Tecton API reference
- Look at the existing feature repository to see if you can reuse Tecton Entities and Data Sources. If you find good fits, confirm with the user that they want to reuse.
- Then, implement the solution
- CRITICAL: Before writing or editing feature code
– You MUST call mcp_tecton_query_example_code_snippet_index_tool to look for relevant code snippets
- You MUST then think about all the available Tecton classes that you will use to implement the feature – You MUST then call mcp_query_tecton_sdk_reference_tool to look at the exact definition of all the Tecton classes and functions you're planning to use
- You MUST finally come up with a plan that explain which parameters of BatchFeatureView, StreamFeatureView or RealTimeFeatureView you're planning to use based on the SDK reference
- When you create a feature that aggregates over time windows of 60 minutes or less, always default to a StreamFeatureView unless explicitly specified otherwise
- You must never combine
AttributeandAggregatefeatures in the same FeatureView - In a feature transformation, you must never reference the current date time using a function like SQL
CURRENT_DATE()or their equivalents in PySpark or Python. Instead, you must use the Tecton providedend_timethat you get from the context parameter which is passed into the transformation function. - Be very thoughtful when you change a customer provided SQL statement. Make sure you don't remove anything that may be relevant to the feature transformation.
- If you create a feature that reads from Snowflake, make sure to use a
BatchSourcewhosebatch_configis set to an instance of aSnowflakeConfig - CRITICAL: If you define a BatchFeatureView that's using a GROUP BY statement (or a similar grouping mechanism) in its transformation function, you must set the
incremental_backfillsparameter to True if you - If you set
incremental_backfillsto True, you must make sure that all sources in thesourcesparameter are referenced by calling theunfiltered()function on them. This ensures that Tecton doesn't filter the data sources by their timestamp before invoking the FeatureView function. You will be responsible for filtering the data manually in the FV function. - CRITICAL: If a user provides a SQL query for a feature: You MUST NOT translate the SQL logic into another language or API (e.g., PySpark DataFrames, Pandas) unless the user explicitly asks you to 'translate to PySpark' or 'rewrite using PySpark DataFrames'. Stick to SQL - it's ok to translate from say Snowfalke SQL to Spark SQL if necessary.
- CRITICAL: Before using a class from the Tecton SDK, make sure you read Tecton's API Reference and know what parameters are required and which ones are optional so you're not hallucinating non-existing parameters and so you're not omitting required parameters.
- You must never have a Python-comment (prefixed with a "#") in a SQL statement
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.
- 2d ago First seen · 97 lines · 11 tokens per session scan A 271e3de35d39
tecton is a cursor rule published in the GitHub repository tecton-ai/tecton-mcp (3 stars, last pushed 1y ago), licensed MIT. It adds 11 tokens to every session and 1,589 once invoked, about $0.0001 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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