Causal Decision Agent

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meta_analysis

Read-only

Combine per-study effect sizes and standard errors into a pooled estimate using fixed- or random-effects meta-analysis, returning confidence intervals, heterogeneity diagnostics, and a forest plot.

Instructions

Summary-data meta-analysis with fixed- and random-effects pooling. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`se`	Yes	Per-study standard errors (must be positive).
`alpha`	No	Significance level for confidence/prediction intervals.
`detail`	No	Payload 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
`labels`	No	Study labels for the forest plot.
`method`	No	Which model the headline ``estimate`` reports: DerSimonian-Laird random effects (default) or fixed-effect inverse-variance. Both are always computed and available on the result.	DL
`effects`	Yes	Per-study effect sizes (e.g. log odds ratios, mean differences).
`as_handle`	No	If 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_path`	No	Absolute 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_id`	No	Optional 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_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 15, 2026

TDQS

B3.2/5.0

Behavior3/5

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

The description adds that both fixed- and random-effects pooling are supported and mentions validation, which is some behavioral context beyond the readOnlyHint annotation. However, 'certified parity evidence' is vague, and the description does not disclose what output the caller should expect or any limitations.

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

Conciseness4/5

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

The description is very short and front-loaded with the core purpose. The validation tag is compact but somewhat cryptic, so it does not fully earn its place; still, the overall structure is efficient given the rich schema.

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

Completeness3/5

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

With a rich 11-parameter schema, full parameter documentation, an output schema, and readOnly annotations, the tool is usable without extensive prose. However, the description lacks any usage context, alternative routing, or behavioral expectations, leaving the agent to infer the tool's role from its name and schema alone.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters, including method, detail, data_path, and result_id. The description itself adds no parameter-level meaning beyond calling the data 'summary-data,' which weakly maps to the required effects and se arguments.

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

Purpose4/5

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

The description clearly states that the tool performs summary-data meta-analysis with fixed- and random-effects pooling, naming the resource and the statistical approach. It is specific enough to distinguish it from most sibling tools, though it does not explicitly contrast it with any alternative meta-analysis-related tool.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool versus alternatives. The phrase 'Validation: certified parity evidence' does not help an agent choose between meta_analysis and any related method or decide on prerequisites.

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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