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vikranthviki

Causal Decision Agent

by vikranthviki

bibtex

Read-only

Look up verified BibTeX entries from the StatsPAI citation database using bib keys; unknown keys return close matches to avoid fabricated references.

Instructions

Return verified BibTeX entries from paper.bib (StatsPAI's single source of truth for citations). Pass one or more bib keys (e.g. 'callaway2021difference'). NEVER invent citations -- call this tool instead. Unknown keys return an empty entry plus a list of close matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysYesBib keys to look up. Most estimators advertise their key in agent_card.reference.
detailNoPayload depth: 'minimal' (~150 tokens) for sub-step calls where only the point estimate is needed; 'standard' (~1K tokens) for diagnostics + coefficient table; 'agent' (~2K tokens, default) adds violations / next_steps / suggested_functions so the LLM can plan its next call without another round-trip.agent
as_handleNoIf true, cache the fitted result on the server and return result_id + result_uri alongside the JSON payload so a subsequent tools/call can chain without re-running.
data_pathNoAbsolute path or URL to a data file. Supported: .csv / .tsv / .txt (delimited), .parquet / .pq, .feather / .arrow, .xlsx / .xls, .dta (Stata), .json / .jsonl. Schemes: file://, s3://, gs://, https://.
result_idNoOptional handle to a previously-fitted result (returned by an earlier call when as_handle=true). Tools that operate on a fitted object accept this in place of re-supplying data_path + columns.
data_columnsNoOptional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
data_sample_nNoOptional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds valuable behavioral context: it confirms it returns verified entries, warns against inventing citations, and specifies that unknown keys produce an empty entry plus close matches. This goes beyond the annotations and helps the agent handle edge cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with zero filler. The core purpose and usage are front-loaded, the warning about inventing citations is prominent, and the unknown-key behavior is stated concisely. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple lookup tool with an output schema (not shown) and read-only annotations, the description covers the essential aspects: what it returns, how to invoke it (with an example), and the edge-case behavior. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all parameters. The description adds an example bib key ('callaway2021difference') and clarifies that multiple keys can be passed, which reinforces the keys parameter. It does not explain the other parameters (detail, as_handle, etc.), but they are well-documented in the schema, so the description adds marginal value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('Return'), a precise resource ('verified BibTeX entries from paper.bib'), and includes an example key. It is unambiguously distinct from the statistical siblings; the only related tool, bib_for, is not mentioned, but the description's citation-specific scope makes it stand apart.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a strong usage directive: 'NEVER invent citations -- call this tool instead.' This tells the agent exactly when to use it (whenever a citation is needed) and implies when not to (do not fabricate). It also mentions the behavior for unknown keys, which guides error handling. It does not name an alternative tool, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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