edgar-mcp

search_metrics

Search SEC EDGAR financial filing metrics using natural-language queries to find relevant data points from company reports.

Instructions

Search available filing metrics by natural-language query and return ranked candidates.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticker": "<ticker>",
      "year": "<year>",
      "quarter": "<quarter>",
      "query": "<query>"
    },
    "name": "search_metrics"
  }
}

Input Schema

Name	Required	Default
`ticker`	Yes
`year`	Yes
`quarter`	Yes
`query`	Yes
`full_year_mode`	No
`source`	No	auto
`date_type`	No
`limit`	No
`include_values`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Feb 24, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'search' and 'return ranked candidates,' which implies a read-only operation with ranking, but it lacks details on permissions, rate limits, error handling, or what 'ranked candidates' entails. For a tool with 9 parameters and no annotation coverage, this is insufficient.

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?

The description is a single, well-structured sentence that efficiently conveys the core functionality. It's front-loaded with the main action and outcome, with no wasted words or redundancy, making it easy to parse quickly.

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?

Given the complexity (9 parameters, 0% schema coverage, no annotations) and the presence of an output schema, the description is incomplete. It covers the basic purpose but lacks parameter explanations, usage context, and behavioral details. The output schema may help with return values, but overall, it's inadequate for a tool of this complexity.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It only references 'natural-language query,' which maps to the 'query' parameter, but ignores the other 8 parameters (e.g., ticker, year, quarter, source). This leaves most parameters unexplained, failing to add meaningful semantics beyond the bare schema.

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 the tool's purpose: 'Search available filing metrics by natural-language query and return ranked candidates.' It specifies the action (search), resource (filing metrics), and method (natural-language query). However, it doesn't explicitly differentiate from sibling tools like 'list_metrics' or 'get_metric', which appear related to metrics retrieval.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_metrics' or 'get_metric', nor does it specify prerequisites, exclusions, or contextual cues for selection. Usage is implied only by the action described.

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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