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Glama

Interpro

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

A5/5.0
Behavior5/5

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

The description reveals specific behavioral traits beyond the readOnlyHint, including fan-out to SEC EDGAR, GDELT→GNews fallback with conditions, and USPTO patent soft-failure due to API sunset. It clearly states the return format (structured changes[], total_changes, citation URIs), adding significant transparency not present in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured, leading with concrete user phrases, then explaining data sources, parameter formats, and return objects, all in a compact block. Every sentence adds value—there is no filler or redundancy, making it highly efficient for an AI agent to parse.

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 absence of an output schema, the description compensates by explicitly listing the return structure (changes[], total_changes, citation URIs) and the fallback logic. It also covers all required inputs, the supported entity type, and provides an alternative tool reference, making the description fully self-contained for effective tool invocation.

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?

While the input schema already covers all three parameters (100% coverage), the description enriches them with practical examples and guidance: `since` accepts ISO dates or relative shorthand, `value` can be a ticker like 'AAPL' or zero-padded CIK, and `type` is restricted to 'company.' It also advises typical usage like '30d' for monitoring, surpassing schema descriptions.

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 identifies the tool as a change feed for a company over a specified time window, using natural-language examples like 'What's new with X' and 'updates on Acme.' It explicitly distinguishes itself from the sibling entity_profile tool by noting the latter provides 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?

It provides explicit instruction on when to use the tool (e.g., for 'latest on Y' or 'what happened to Z'), and directly suggests an alternative (entity_profile) when a static profile is needed. It also explains the `since` parameter with recommended formats like '30d' or '1m', giving clear usage direction.

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

A3.6/5.0
Disambiguation2/5

Several tool clusters have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap on 'find and query data,' while the five polymarket_* tools plus bet_research form a heavily overlapping prediction-market cluster. The three genuine InterPro tools are clear, but an agent would frequently struggle to pick the right meta-tool.

Naming Consistency3/5

Most tools follow a readable snake_case verb-first pattern like compare_entities, resolve_entity, and validate_claim. However, bare verbs (remember, forget, recall), product-prefixed nouns (pipeworx_feedback, pipeworx_trending), and variant suffixes (ask_pipeworx_beta, ask_pipeworx_grounded) break the pattern enough to feel inconsistent.

Tool Count2/5

34 tools is well above the typical well-scoped range, and many tools duplicate or partially overlap each other's functionality. The count is further inflated by unrelated domains—AI visibility, prediction markets, memory, subscriptions, package auditing—bundled into a server nominally named 'Interpro.'

Completeness2/5

The InterPro subset (search_entries, get_entry, entries_for_protein) is minimal and lacks obvious protein/proteome-level operations, while the rest of the server covers so many unrelated domains that no single domain has clear end-to-end coverage. The Pipeworx query side is broad, but the overall surface feels like several incomplete toolsets merged rather than one complete product.

Resources