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extract_gov_landscape

Read-only

Composite government intelligence: federal contract awards (USASpending) + dev community awareness (HN) + GitHub repo activity + product release velocity (changelog). 4-source unified report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesCompany name, keyword, or NAICS code
github_urlNoOptional GitHub repo URL for the company

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description need not reiterate safety. The description adds useful behavioral context beyond annotations: it explicitly reveals that the tool aggregates data from multiple distinct APIs into a single report, which is a non-obvious behavioral trait. No contradictions with annotations.

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, information-dense sentence with a clear list of sources. It is efficient with no redundant words, though it could be slightly improved by separating the sentence or adding clarity on the report structure. Overall, it is concise and well-structured.

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 there is no output schema, the description should explain the return value more thoroughly. While it lists the four components of the report, it does not describe the report's format, key metrics, or potential limitations. This is adequate but leaves gaps for an agent needing to interpret the output.

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% for both parameters (query and github_url), so the schema fully describes them. The description does not add additional parameter-specific meaning beyond what is already in the schema, so the baseline score of 3 is appropriate.

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 the tool's purpose: to produce a composite government intelligence report combining four specified sources. It uses a specific verb ('extract' implied by the name) and resource ('government intelligence'), and the enumeration of sources distinguishes it from sibling extraction tools that focus on a single source.

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?

The description gives clear context on what the tool covers (four sources) and implies it is the unified version when multiple sources are needed, but it does not explicitly name alternatives or exclusion criteria. Agents can reasonably infer when to use this tool versus single-source siblings.

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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TDQS

A3.9/5.0
Disambiguation5/5

Each tool targets a distinct data source (finance, GitHub, Hacker News, etc.), with clear separation and no overlap. An agent can easily distinguish which tool to use for a given source.

Naming Consistency4/5

Tools use a consistent verb_noun pattern with 'extract_' for data extraction and 'search_' for search functions. The outlier 'package_trends' is still descriptive and fits the theme, so the pattern is mostly predictable.

Tool Count5/5

11 tools is well-scoped for a data aggregation server. Each tool serves a clear purpose and the count is neither too sparse nor overwhelming.

Completeness4/5

The server covers a broad range of sources (finance, code, news, social, academia, jobs, packages). Minor gaps like missing Twitter or general news are acceptable given the breadth already provided.