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Glama

Textcase

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable context: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas... each result is ready to call directly.' This discloses output behavior and usability beyond 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 front-loaded with purpose and usage, then details the output. It is efficient but slightly lengthy due to listing many domains. Every sentence earns its place, so it's well-structured.

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 no output schema, the description fully explains the return format (names, descriptions, schemas with examples) and behavior (top-N, ready to call). It covers all necessary context for an agent to correctly invoke and interpret results.

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%, so baseline is 3. The description does not add significant new meaning to parameters beyond what the schema provides (e.g., aliases are listed in schema). However, it reinforces the natural language usage of the 'query' parameter.

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: 'Find tools by describing the data or task.' It provides a specific verb and resource, and distinguishes itself from sibling tools by being the primary discovery tool for browsing available options.

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?

Explicitly advises to 'Call this FIRST when you have many tools available and want to see the option set.' It also lists use cases like browsing or searching for tools in various domains, providing clear when-to-use guidance.

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

All Textcase toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to data sources; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all target prediction-market analysis; and all_cases plus convert_case both handle text-case conversion. An agent will struggle to pick the right tool without deep reading.

Naming Consistency3/5

Nearly all names are lowercase with underscores, but the morphological pattern is mixed: some are verb_noun (convert_case, compare_entities, resolve_entity, scan_dependency), many are bare nouns (entity_profile, pipeworx_trending, polymarket_edges, all_cases), and a few are single-word verbs (forget, recall, remember). Still readable, but not a predictable verb_noun convention throughout.

Tool Count2/5

33 tools is far too many for a server named 'Textcase' — only all_cases and convert_case relate to the apparent purpose. The remaining 31 constitute a sprawling assortment of data research, prediction markets, memory, subscriptions, and feedback tools that have nothing to do with text casing, making the count feel bloated and misaligned.

Completeness2/5

For the stated text-case domain, the two converters cover the basic transformations but lack supporting operations like case detection, batch processing, or custom case definitions. More fundamentally, the tool surface is incoherent: the majority of tools serve foreign domains (Pipeworx data, Polymarket, subscriptions), so there is no clear domain to evaluate for completeness, and obvious gaps exist within whatever the server is meant to be.

Resources