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Glama

Census

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

A4.9/5.0
Behavior5/5

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

Annotations already indicate safe read operations. Description adds significant behavioral context: fan-out to multiple APIs, fallback from GDELT to GNews, PatentsView sunset soft-fail, and return structure. Goes well beyond annotation defaults.

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 lengthy but every sentence serves a purpose. It is front-loaded with usage examples. Could be slightly tighter, but remains efficient given 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?

Given the tool's complexity (multiple data sources, fallback logic, parameter variations, no output schema), the description covers all necessary context: sources, fallback, parameter formats, return structure, and limitations like PatentsView sunset.

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 already has 100% coverage with descriptions. Description enriches parameters: explains type enum limitation, provides multiple valid since examples with both absolute and relative formats, and clarifies value as ticker or zero-padded CIK.

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 it is a change feed for a company, listing sources like SEC EDGAR, GDELT/GNews, and USPTO. It distinguishes from sibling tool entity_profile by contrasting dynamic changes versus static profile.

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?

Explicitly provides query examples ("What's new with X", "latest on Y") and states when to use entity_profile instead for static information. Gives clear context for appropriate invocation.

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
Disambiguation2/5

Multiple tools appear to do the same thing: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all provide natural-language data lookup, with ask_pipeworx_beta explicitly identical to ask_pipeworx. The six Polymarket tools also have heavily overlapping boundaries, and the Census-specific tools overlap with each other and with ask_pipeworx's routing to Census data.

Naming Consistency3/5

All names are lowercase snake_case, which is consistent, but the pattern is a mix: some are verb_noun (discover_tools, generate_llms_txt, list_subscriptions) while many are noun_verb or bare noun phrases (entity_profile, pipeworx_feedback, polymarket_edges, census_acs). Suffixes like _beta, _grounded, and the scattered noun-first names prevent a uniform convention.

Tool Count2/5

36 tools is well into the too-heavy range for a coherent server. Even if the domain truly is broad data research and prediction markets, many of these tools are near-duplicates or serve the same purpose with minor variations, so the count feels inflated rather than scoped.

Completeness4/5

The broad data-research domain is well covered: single lookups (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), multi-source research (deep_research, entitity_profile, compare_entities, recent_changes), plus memory and subscription utilities. Minor gaps exist (e.g., no general data update/delete, but data is read-only; no direct Census variable explorer beyond census_available_datasets), but no critical dead ends appear.

Resources