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Glama

Senate Lobbying

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

A4.4/5.0
Behavior4/5

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

Discloses parallel calls, fallback from GDELT to GNews, and soft-failure for USPTO. Annotations already declare readOnlyHint, idempotentHint, destructiveHint false; description adds operational detail without contradiction.

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 about 100 words, front-loaded with example queries, and every sentence adds value (source details, parameter nuances, sibling reference). Concise for the complexity.

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?

Despite no output schema, description adequately describes return structure (changes[] grouped by source, total_changes count, citation URIs). Covers data sources, fallback, soft-fail, and parameter specs fully.

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

Parameters4/5

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

Schema coverage is 100% (baseline 3), but description adds meaning: explains 'since' accepts ISO or relative formats with examples, clarifies 'value' can be ticker or CIK, and notes 'type' is currently limited to 'company'.

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 tool provides a 'change feed for a company' aggregating from SEC, GDELT/GNews, USPTO. Specific verbs like 'fans out' and 'returns structured changes' clarify the action. Distinguishes from sibling entity_profile.

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?

Provides explicit usage examples ('What's new with X', etc.) and advises using entity_profile for static profiles. Gives parameter guidance for 'since'. Lacks explicit when-not-to-use scenarios.

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

A3.8/5.0
Disambiguation3/5

Several tools form overlapping clusters: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route to the same underlying data catalog, and the five polymarket_* tools all serve prediction-market edge detection with heavily overlapping purposes. Individual descriptions are detailed enough to disambiguate with careful reading, but an agent could easily pick the wrong one, especially since ask_pipeworx_beta is currently identical to ask_pipeworx.

Naming Consistency4/5

The vast majority of tools follow a readable lower_snake_case verb_noun pattern (search_filings, get_filing, compare_entities, resolve_entity). Minor deviations exist: bare-verb tools like remember/recall/forget/subscribe/unsubscribe break the pattern, and ask_pipeworx is more brand-like than descriptive, but overall the convention is predictable and consistent.

Tool Count2/5

34 tools is heavy for a single MCP server, particularly one named 'Senate Lobbying' with only 3 tools (search_filings, get_filing, list_issue_codes) actually serving that purpose. The remaining 31 tools form a sprawling general-purpose data platform with discovery, memory, subscription, AI-visibility, and prediction-market tooling that has no clear relationship to the server's stated name.

Completeness3/5

For the nominal LDA lobbying domain, the read-only surface is functional: search_filings covers querying, get_filing provides detail, and list_issue_codes supports filtering. However, the server's actual breadth is a general data platform, and within that broader scope there are gaps such as no direct tool to fetch a pipeworx:// citation URI and several sources that soft-fail (patents), plus the deep_research tool requiring an account not mentioned for most other tools.

Resources