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Glama

Gov Bids

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds granular behavioral context: fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-failure note, and output structure (changes grouped by source, total_changes, citation URIs). No contradictions.

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?

Description is a single dense paragraph but front-loads key behavior and includes many examples. While every sentence adds value, it is slightly verbose and could be more structurally organized.

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?

Given the complexity (multiple data sources, fallback, output format), the description covers all essential aspects: input constraints, behavior, output structure, and alternative tools. Since no output schema exists, the description explains the return format adequately.

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

Parameters5/5

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

Schema has 100% coverage with descriptions for all 3 parameters. Description adds further value by explaining supported formats for `since` (ISO date and relative shorthand like '30d', '1y'), and clarifies that `type` only supports 'company'. This goes beyond the schema.

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?

Description clearly states the tool returns a change feed for a company in a recent time window, with specific examples like 'What's new with X' and 'latest on Y'. It distinguishes itself from sibling tool 'entity_profile' by explicitly stating when to use that instead.

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

Usage Guidelines5/5

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

Provides explicit guidance on when to use this tool vs alternative 'entity_profile' for static profiles. Explains fan-out to multiple data sources, fallback behavior (GDELT→GNews), and gives parameter usage examples such as '7d' or '30d' for the `since` parameter.

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
Disambiguation4/5

Most tools have clearly distinct purposes, but the multiple Pipeworx query variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and multiple Polymarket tools could cause some confusion. However, descriptions are detailed enough to differentiate them.

Naming Consistency3/5

Naming patterns are mixed: some tools use verb_noun (list_subscriptions, open_bids_search), others use noun_phrase (entity_profile, bet_research), and cases are inconsistent (snake_case vs underscores). While not chaotic, the lack of a strong consistent pattern reduces coherence.

Tool Count2/5

33 tools is high, and the server's name 'Gov Bids' suggests a focused scope, but most tools are unrelated (AI visibility, npm packages, general data queries). The tool count feels excessive for a focused server, and the scope is too broad.

Completeness4/5

For a general-purpose data server, the tool set is quite comprehensive across multiple domains (SEC, FDA, economics, prediction markets, etc.). Minor gaps exist (e.g., government contracts beyond bids), but overall coverage is strong.

Resources