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henrysouchien

edgar-mcp

get_metric

Retrieve financial metrics from SEC EDGAR filings to analyze company performance with current/prior values and YoY comparisons.

Instructions

Get a specific financial metric by common name or XBRL tag and return current/prior values with YoY comparison.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYes
yearYes
quarterYes
metric_nameYes
full_year_modeNo
sourceNoauto
date_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 the tool returns 'current/prior values with YoY comparison,' which hints at read-only behavior, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or data freshness. For a financial data tool with no annotation coverage, this is a significant gap.

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 a single, well-structured sentence that efficiently conveys the core purpose. It's front-loaded with the main action and avoids unnecessary details. However, it could be slightly more concise by integrating parameter hints more directly, but overall it's appropriately sized.

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 tool's complexity (7 parameters, financial data) and the presence of an output schema (which likely covers return values), the description is moderately complete. It states the purpose and output format but lacks usage guidelines, detailed parameter semantics, and behavioral context. With no annotations and low schema coverage, it should do more to be fully adequate.

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%, meaning none of the 7 parameters are documented in the schema. The description only vaguely references 'common name or XBRL tag' (related to 'metric_name') and implies temporal aspects ('current/prior values'), but doesn't explain parameters like 'ticker', 'year', 'quarter', 'full_year_mode', 'source', or 'date_type'. It adds minimal value beyond the schema, failing to compensate for the coverage gap.

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: 'Get a specific financial metric by common name or XBRL tag and return current/prior values with YoY comparison.' This specifies the verb ('Get'), resource ('financial metric'), and scope ('return current/prior values with YoY comparison'). However, it doesn't explicitly differentiate from sibling tools like 'list_metrics' or 'search_metrics', which likely serve different purposes.

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' (which might list available metrics) or 'search_metrics' (which might search metrics), nor does it specify prerequisites or exclusions. Usage is implied by the purpose but lacks explicit context.

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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