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Glama

Data Gov In

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

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnly/idempotent/not-destructive annotations by disclosing data source fan-out (SEC EDGAR, GDELT→GNews fallback, USPTO), specific fallback conditions (rate-limited or 5xx), and the USPTO PatentsView sunset causing soft-fail. It also describes the return structure (changes[] grouped by source, total_changes, citation URIs), adding valuable 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?

The description is dense but information-dense; every sentence contributes meaning, including source behavior, fallbacks, parameter syntax, and return format. It is slightly longer than ideal but appropriate for a multi-source aggregator with important caveats.

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 no output schema, the description compensates by explaining the return shape, grouped sources, count, and citation URIs. It also covers parameter formats, source fallbacks, and a clear alternative, making it complete enough for correct selection and invocation.

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?

Input schema already covers all parameters with 100% description coverage, so the baseline is 3. The description adds minor clarifications like 'Use 30d or 1m for typical monitoring' and ticker/CIK examples, but these mostly restate schema info rather than introduce new semantics.

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 is a 'change feed for a company' covering filings, news, and patents, with example queries ('What's new with X', 'updates on Acme'). It distinguishes itself from sibling entity_profile by explicitly directing users to that tool for static profiles.

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?

It provides explicit when-to-use contexts through natural language examples and names an alternative ('Use entity_profile instead when you want the static profile... regardless of window'). This gives clear guidance for selecting between recent_changes and related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Gov In toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language data questions. The six Polymarket/prediction-market tools also blur together, and ai_visibility_check versus scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Snake_case is used consistently, and most tools follow a verb-first or domain-prefixed pattern (ask_pipeworx, compare_entities, subscribe, polymarket_*). Minor deviations like entity_profile, resource_data, and ai_visibility_check are noun-first, but nothing is chaotic or mixed-cased.

Tool Count2/5

33 tools is excessive for a server named 'Data Gov In' whose actual domain-specific surface is only resource_data and resource_meta. The rest are generic Pipeworx, prediction-market, memory, and utility tools that do not belong to the apparent India open-data scope.

Completeness1/5

For a data.gov.in server, the surface is severely incomplete: there is no way to search or list datasets/resources, only fetch metadata and data for a known resourceId. The overwhelming majority of tools serve unrelated domains, so an agent using this server for Indian government data will hit dead ends immediately.

Resources