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Glama

dogceo

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?

Beyond the readOnly/openWorld/idempotent annotations, the description discloses fan-out to SEC EDGAR, GDELT→GNews with a specific fallback trigger (rate-limited or 5xx), and a soft-fail for USPTO due to API sunset. This gives the agent realistic expectations for data sourcing and failure modes.

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 long but tightly packed; the opening paraphrases quickly give way to a precise definition. Every sentence contributes source behavior, fallback logic, return shape, or an alternative tool. It is not overly verbose for the amount of useful operational detail it conveys.

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?

Even without an output schema, the description explains the return structure (changes[] grouped by source, total_changes, citation URIs), external API dependencies, and a sibling alternative. This is complete enough for an agent to predict and interpret results effectively.

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 of parameters is 100%, so the baseline is 3. The description adds value by providing concrete examples for `since` (ISO date and relative shorthand), clarifying how each source uses the window, and reinforcing that `value` can be a ticker or CIK. This is modest enrichment over the schema, warranting a 4.

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 opens with a clear action verb and resource: 'change feed for a company in the last N days/weeks/months.' It provides multiple example user intents and explicitly distinguishes itself from the sibling entity_profile tool, making selection unambiguous.

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?

The description states exactly when to use this tool versus an alternative: 'Use entity_profile instead when you want the static profile...' It also describes the GDELT→GNews fallback behavior and notes typical monitoring windows, giving an agent clear contextual guidance.

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

A3.7/5.0
Disambiguation2/5

Several tools deliberately overlap: ask_pipeworx and ask_pipeworx_beta are currently identical, and the dog-photo trio plus a cluster of six prediction-market tools creates real selection ambiguity. Although descriptions are detailed, an agent could easily call the wrong variant.

Naming Consistency3/5

All names are consistently snake_case and readable, with useful domain prefixes like polymarket_ and ask_pipeworx_. However, conventions are mixed: compare_entities is verb-first, entity_profile is noun-first, bet_research is object-verb, and random_image is adjective-noun.

Tool Count2/5

35 tools is too many for a server whose name suggests a simple dog-photo service, and most tools are unrelated to that identity. The scatter across dog images, deep data research, prediction markets, npm scanning, memory, and llms.txt generation makes the set feel bloated rather than comprehensive.

Completeness3/5

The dog-image functionality is complete, and the subscription and memory lifecycles have paired operations. However, the server's true domain is incoherent, so completeness is difficult to assess; there are no major dead-ends within each cluster, but the unrelated utility tools create large topical gaps relative to the apparent dogceo identity.

Resources