Skip to main content
Glama

Devdocs Io

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?

The description discloses significant behavior beyond annotations: it fans out to SEC EDGAR, GDELT, GNews, and USPTO; explains fallback logic (GNews on rate limits/5xx); notes the PatentsView API sunset causing soft-fails; and describes the return structure (changes[], total_changes, citation URIs). This complements the readOnlyHint and idempotentHint annotations 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?

The description is dense but well-structured: it leads with use-case examples, then explains sources and fallbacks, parameter behavior, return format, and ends with an alternative tool pointer. While slightly long, every sentence contributes useful information without redundancy, making it appropriately sized for the tool's 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 external sources, fallbacks, date parsing, return format) and no output schema, the description is remarkably complete. It covers all key aspects: inputs, output structure, source-specific behavior, failure modes, and explicit alternative routing. No significant gaps remain.

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 coverage is 100% for all three parameters, so the baseline is 3. The description repeats parameter values and adds the context that `since` accepts ISO dates or relative shorthand (already in schema) and that `type` currently only supports "company". It does not add meaningful semantics beyond what the schema already documents, so a 3 is appropriate.

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 what the tool does: it provides a change feed for a company over a specified time window, aggregating filings, news, and patents. It uses specific verbs and resources ("change feed") and distinguishes itself from sibling tool entity_profile by explicitly saying to use that instead for static profiles.

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 extensive usage context: example queries ("What's new with X"), acceptable date formats, source fallback behavior (GDELT→GNews), and explicit guidance to use entity_profile for static profile needs. It also mentions the tool works in one parallel call and includes typical monitoring suggestions like "30d or 1m".

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Devdocs Io toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.1/5.0
Disambiguation3/5

There are many tools with overlapping purposes (e.g., multiple ways to ask questions, multiple Polymarket analysis tools, multiple company lookup tools). The detailed descriptions help distinguish them, but an agent could still easily select the wrong one.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun patterns (ask_pipeworx, search_docs) with single words (db, docs) and compound names (polymarket_arbitrage, ai_visibility_check). No clear convention across the set.

Tool Count2/5

37 tools is excessive for a DevDocs documentation server; most tools are unrelated to documentation (Pipeworx data, memory, subscriptions). The core documentation functionality only requires about 7-8 tools, making the rest feel extraneous.

Completeness3/5

For the core DevDocs functionality, the tools cover listing, searching, and fetching documentation. However, the server includes many unrelated tools that are incomplete on their own (e.g., only some data lookups, no CRUD for prediction markets). Thus overall completeness is mediocre.

Resources