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Glama

Jwt

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

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint, destructiveHint, idempotentHint; the description adds that it returns full input schemas with curated examples ready to call directly. No contradictions. The description enriches understanding 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.

Conciseness5/5

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

The description is front-loaded with the core purpose. Every sentence adds value: specifying use cases, listing data domains, explaining the return format, and strategic advice. It is dense but not verbose.

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 discovery tool with no output schema, the description adequately explains what is returned (names, descriptions, schemas). It covers the tool's role in the workflow, making it complete for an agent to understand and invoke.

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% with good descriptions. The description adds that query accepts multiple aliases (task, q, description, search) and clarifies the intent of the query parameter as a natural language description. This adds value beyond the schema.

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 that the tool finds tools by describing a data or task, and returns top-N relevant tools with full schemas. It distinguishes itself from sibling tools by positioning it as the first tool to call when many are available.

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 says 'Use when you need to browse, search, look up, or discover what tools exist for...' and lists many domains. Also advises 'Call this FIRST when you have many tools available and want to see the option set.' This provides 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 Jwt toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have unclear boundaries. ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language queries to the same underlying catalog, with ask_pipeworx_beta currently documented as identical to ask_pipeworx. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also overlaps in purpose, leaving an agent to parse fine-grained differences before selecting.

Naming Consistency3/5

All tool names use snake_case and several share recognizable prefixes (ask_pipeworx_*, polymarket_*), which helps readability. However, the verb/noun order is inconsistent—compare_entities vs entity_profile, bet_research vs deep_research, scan_competitor_ai_presence vs ai_visibility_check—and bare verbs like remember, forget, and subscribe mix with noun-first names.

Tool Count2/5

32 tools is well above the 25-tool threshold for a coherent set. The bloat is worse because the server is named 'Jwt' but only one tool relates to JWT; the rest cover unrelated domains such as data routing, prediction markets, memory, subscriptions, and npm scanning, so the count is neither scoped to the server's name nor internally cohesive.

Completeness2/5

For a server named 'Jwt', the surface is severely incomplete: only decode_jwt is present, with no sign, verify, encode, or refresh tools, so common JWT workflows dead-end. Even when judged as a general Pipeworx data toolkit, the mismatch between the server name and the actual tool surface creates a significant gap for agents expecting JWT functionality.

Resources