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Glama

Csv

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds details about fan-out to multiple sources, fallback logic, patent soft-fail, and return structure with citation URIs, enhancing transparency 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.

Conciseness5/5

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

The description is well-structured, starting with common queries, then explaining the tool's behavior, parameter details, and a sibling note. Every sentence adds value without redundancy.

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 no output schema, the description thoroughly covers data sources, fallbacks, return format, and parameter usage. It also provides a clear alternative, making it complete for agent decision-making.

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 is 100% and each parameter has a description. The tool description adds value by clarifying the 'since' format with examples and noting that 'value' can be ticker or CIK. It does not introduce new parameters but enriches understanding.

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 specifies the tool as a change feed for companies, fetching SEC filings, news, and patents in one parallel call. It distinguishes from the sibling tool 'entity_profile' by stating when to use that instead.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage examples ('What's new with X') and a clear alternative (entity_profile for static profile). While it does not exhaustively list when not to use, the context is sufficient for most cases.

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

Several tools overlap in purpose: ask_pipeworx_beta is explicitly identical to ask_pipeworx today, and the polymarket_* family plus bet_research all touch prediction-market analysis. The descriptions are extremely detailed and mostly disambiguate, but an agent must rely on very long text to avoid misselection.

Naming Consistency3/5

Names are almost all snake_case and readable, but the pattern is mixed: some are verb-first (resolve_entity, list_subscriptions), some noun-first (entity_profile, polymarket_edges), and some are one-word verbs (remember, forget). The ask_pipeworx_* and recent_* prefixes are consistent, but there is no single verb_noun convention throughout.

Tool Count2/5

33 tools is well over the 25+ threshold, especially for a server named 'Csv' that contains only two CSV-specific tools. The rest spans several unrelated domains: data research, prediction markets, subscriptions, memory, AI visibility, and package scanning. The set feels like multiple servers bundled together rather than one well-scoped surface.

Completeness4/5

As a broad data-research platform, coverage is strong: lookup, grounded answers, deep research, entity resolution, profiles, comparisons, fact-checking, subscription lifecycle, and memory persistence are all present. Minor gaps exist, such as no direct fetch tool for pipeworx:// citation URIs and no subscription-update tool, but most workflows have no dead ends.

Resources