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Glama

Datos Co

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

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint false, which match the description. The description adds important behavioral details: fans out to SEC EDGAR, GDELT→GNews fallback (with conditions), and USPTO (with soft-fail note). It also describes output format (changes[], total_changes, citation URIs). No contradiction with annotations.

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 relatively long but efficiently packs example queries, source details, parameter guidance, and usage alternatives. It front-loads key information and minimizes fluff. A minor deduction for length; still very concise 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?

For a complex tool with multiple sources, fallback logic, and no output schema, the description is thorough. It mentions return structure (changes[], total_changes, citation URIs), source priorities, and API statuses. Combined with annotations, it leaves little ambiguity.

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 coverage is 100% with parameter descriptions. The description adds value beyond the schema: explains 'since' accepts ISO date or relative shorthand with examples ('7d', '30d', '3m', '1y'), and 'value' accepts ticker or CIK. It also clarifies 'type' is limited to 'company'.

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 the tool's purpose: 'change feed for a company in the last N days/weeks/months'. It opens with example queries ('What's new with X', 'latest on Y'), which immediately signal intent. It explicitly differentiates from sibling tool 'entity_profile' by stating when to use that instead, avoiding confusion.

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 explicitly says 'Use entity_profile instead when you want the static profile...', providing direct guidance on when not to use this tool and suggesting an alternative. It also implies use cases for tracking changes over time.

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

A3.9/5.0
Disambiguation3/5

Most tools have distinct purposes, but several natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) occupy overlapping territory, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also has fuzzy boundaries, though the long descriptions help an agent differentiate.

Naming Consistency3/5

Names are consistently lowercase with underscores, and there are coherent subfamilies like ask_pipeworx*, polymarket_*, and scan_*. However, the overall set mixes conventions: verb_noun (query_dataset, resolve_entity), noun_noun (entity_profile, bet_research), adjective_noun (recent_changes), and bare verbs (remember, recall, forget), so no single predictable pattern governs the server.

Tool Count2/5

34 tools is a heavy surface for one server, including multiple meta/onboarding utilities (discover_tools, suggest_questions, pipeworx_feedback, pipeworx_trending) and a large prediction-market subcluster. The count exceeds the 25-tool threshold where a tool set typically becomes unwieldy, and several tools could be consolidated or split into separate servers.

Completeness4/5

For a read-heavy data research platform, the surface is very complete: discovery, routed lookup, grounded verification, entity resolution, profiles, comparisons, change feeds, dataset querying, prediction-market research, and full memory/subscription lifecycles are all covered. Minor gaps exist, such as no explicit pipeworx:// URI read tool and no subscription update operation, but agents can work around them.

Resources