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glimind

by io.github.shawndei

discover_tools

Find LIVE tools that can accomplish a task you describe in plain language — call this when you do NOT yet know which tool to use. Unlike find_alternatives / find_related_tools (which need a tool id you already have), this takes a free-text capability query (e.g. 'send a slack message', 'convert currency', 'search arxiv papers') and returns ready-to-use tool ids ranked by semantic similarity, filtered to tools that are live right now — each result carries the tool's advertised input schema, its actual connection endpoint (the MCP endpoint URL, or the package to launch for stdio servers), and whether it is FREE or PAID with the price + how to pay — so you can invoke it immediately without a second lookup or an MCP-registry search (on-demand / MCP-Zero style tool discovery). The discovery entry point at the start of a new task.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 8, max 25).
queryYesPlain-language description of the capability you need (e.g. 'send an email').
categoryNoOptional capability category to narrow results.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

No annotations provided, so description carries full transparency burden. It explains the returned data (tool ids, schema, endpoint, pricing) and filtering (live, ranked). It doesn't explicitly state safety (read-only), but the nature implies no side effects. The description does not conflict with any annotation.

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 somewhat long but every sentence adds value. It front-loads the main purpose and use case. Could be slightly more concise, but it is well-structured and comprehensive.

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 and no annotations, the description covers input, output, behavior, and use case completely. It mentions it's the discovery entry point for new tasks. All necessary information for an AI agent to select and invoke the tool is present.

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%, and description adds meaning beyond schema by providing examples for query ('send a slack message', 'convert currency') and indicating that query is a plain-language description. It also explains the optional limit and category, adding context not in 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 the verb 'Find LIVE tools' and the resource 'tools that can accomplish a task'. It distinguishes itself from sibling tools find_alternatives and find_related_tools by noting that those require a known tool id, while discover_tools takes a free-text query.

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 states 'call this when you do NOT yet know which tool to use', providing clear context. It also contrasts with siblings that need a tool id, giving when-not guidance implicitly.

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

A4.3/5.0
Disambiguation5/5

Each tool has a distinct, well-defined purpose: checking reliability, discovering tools, finding alternatives, getting recipes, routing tasks, etc. No two tools overlap in function.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with underscores (e.g., check_tool_reliability, route_task, watch_tool). The only minor deviation is 'how_to_use_glimind', which still follows a clear verb phrase convention.

Tool Count5/5

With 15 tools, the set is well-scoped for a comprehensive meta-layer covering discovery, reliability checking, preparation, batch routing, reporting, and notifications. Each tool earns its place.

Completeness4/5

The surface covers the full workflow of discovering, checking, preparing, calling, and reporting on tools. A minor gap is the lack of a direct 'list all tools' catalog, but the discovery tools effectively fill this need.

Resources