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Glama

Grants Gov

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Description details multi-source fan-out (SEC EDGAR, GDELT→GNews fallback, USPTO), fallback behavior (GNews when GDELT rate-limited or 5xx), and soft-failure for patents due to API sunset. This complements annotations (readOnly, idempotent, openWorld) with specific behavioral context.

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, front-loaded with example queries. Every sentence adds value, but structure could be improved with bullet points for sources or parameters. Still efficient and clear.

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

Completeness4/5

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

Given lack of output schema, description sufficiently covers sources, grouping, citations, and parameter formats. However, it does not detail the exact structure of the changes array or total_changes count, leaving some ambiguity for an agent.

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 coverage is 100%, but description adds value: explains since parameter accepts ISO dates or relative shorthands, recommends '30d', clarifies type is limited to 'company', and describes value as ticker or CIK. Adds pragmatic usage details beyond 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 specifies the tool's purpose: providing a change feed for a company over a time window, with example queries like 'What's new with X'. It distinguishes from sibling tool entity_profile, which is for static profiles, achieving high specificity.

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?

Explicit guidance on when to use this tool (recent changes feed) versus entity_profile (static profile). Also recommends typical monitoring window ('30d' or '1m') and explains parameter formats.

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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx/ask_pipeworx_grounded/deep_research all handle broad queries; multiple Polymarket tools exist for edge finding; entity_profile/compare_entities/recent_changes/resolve_entity overlap on company data. An agent would struggle to pick the right tool.

Naming Consistency2/5

Naming patterns are mixed: some use verb_noun (search_opportunities, get_opportunity), some are phrases (ask_pipeworx_grounded, polymarket_edge_tracker), and some are vague (recall, forget). No consistent convention across the set.

Tool Count2/5

32 tools is high, but the core issue is that the server name 'Grants Gov' implies a narrow focus, yet only 2 tools are about grants. The sheer number of unrelated tools makes the set feel bloated and unfocused.

Completeness3/5

For the actual domain of general data querying and prediction markets, the tool surface is fairly complete, covering many sources. However, for 'Grants Gov' it is severely incomplete (missing all but opportunities). Overall, the scope is broad but lacks depth in any one area.

Resources