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Glama

Mesh

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?

Beyond the annotations (readOnlyHint, etc.), the description details the parallel fan-out to multiple sources, the GDELT→GNews fallback mechanism, the soft-fail for USPTO due to API sunset, and the return structure (changes by source, total_changes count, citation URIs). It fully discloses behavioral traits.

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 well-structured, front-loading with example queries to quickly convey purpose. It is reasonably concise given the complexity, though slightly verbose with multiple example queries. Every sentence adds value.

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 lack of an output schema, the description thoroughly explains the tool's behavior: sources used, fallback logic, soft-failures, and return format (structured changes, total count, citation URIs). It also clearly distinguishes from the sibling entity_profile, ensuring complete contextual understanding.

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?

Schema description coverage is 100%, so baseline is 3. The description reinforces parameter types and formats but does not add significant new meaning beyond the schema's own descriptions. It provides minor usage guidance (e.g., typical 'since' values) but not deeper 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 opens with clear query examples ('What's new with X', 'latest on Y') and explicitly states it returns a change feed for a company over a time window, listing specific data sources. It distinguishes from sibling tool entity_profile, which is for static profiles regardless of window.

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 provides explicit guidance: when to use (for recent changes over a window), when not to (use entity_profile for static profile), and usage tips for the 'since' parameter ('Use '30d' or '1m' for typical monitoring'). It also names an alternative tool.

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

A4/5.0
Disambiguation3/5

The three ask_pipeworx variants are a genuine confusion risk — ask_pipeworx_beta is currently described as identical to ask_pipeworx, and ask_pipeworx_grounded differs only in extraction strictness. search_descriptors and resolve_term also overlap heavily (both map a term to MeSH descriptor IDs). The six Polymarket tools are differentiated by rich descriptions but still form a dense cluster where misselection is plausible.

Naming Consistency4/5

Most tools follow a clean verb_qualifier pattern (ask_, bet_, compare_, resolve_, search_, validate_) with the brand as a namespace (ask_pipeworx, pipeworx_trending, polymarket_*). Minor deviations exist: pipeworx_feedback and pipeworx_trending lead with a noun, the memory trio (remember, recall, forget) are bare verbs, and the ask_pipeworx family uses a brand name rather than a resource noun — but the overall pattern stays predictable and readable.

Tool Count2/5

At 34 tools this exceeds the threshold where a set feels heavy, and the bloat is visible: three near-identical ask_pipeworx routers and a six-tool Polymarket suite dominate. Several tools (generate_llms_txt, scan_dependency, bet_research) feel like accreted one-offs rather than part of a coherent surface. The broad platform purpose justifies some breadth, but the count is inflated by redundant variants.

Completeness4/5

The core purpose — universal access to authoritative structured data — is well covered via the router, grounded mode, deep_research, entity/profile/compare/validate wrappers, and discovery tools. The subscription lifecycle (subscribe/unsubscribe/list/alerts) and memory (remember/recall/forget) have no dead ends. Minor gaps exist: non-polymarket prediction-market workflows lack the depth of the Polymarket cluster, and the single-purpose niche tools fit awkwardly, but agents won't hit dead ends.

Resources