Skip to main content
Glama
nitinchakravarthy

Workout Tracker MCP Server

search_exercises

Find exercises by name or keyword using fuzzy matching. Returns ranked matches with relevance scoring to help locate workout movements.

Instructions

Search for exercises by name or keyword with fuzzy matching.

Args:
    query: Search query (exercise name or keyword)
    limit: Maximum number of exercises to return (default: 20, max: 100)
    offset: Number of exercises to skip (default: 0)
    threshold: Fuzzy match threshold 0.0-1.0 (default: 0.3, lower = more results)

Returns:
    List of exercises matching the search query with relevance scoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
offsetNo
thresholdNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains fuzzy matching, the threshold's effect ('lower = more results'), and relevance scoring in the return value. It does not mention non-mutation or rate limits, but for a search operation these are less critical and the key behavioral traits are covered.

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 concise and well-structured: a one-sentence summary up front, followed by a compact parameter list and a return-value line. Every sentence carries necessary information, and there is no redundant or filler content.

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 simple search tool, the description provides all essential information needed to call it correctly: what it searches, how fuzzy matching works, parameter meanings and defaults, and what it returns. The presence of an output schema further reduces the need to document return structure in detail.

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 description coverage is 0%, so the description must compensate, and it does. Every parameter is explained: 'query' as name/keyword, 'limit' with a max of 100, 'offset' as skip count, and 'threshold' with a range and behavioral implication. This adds real semantic value beyond the raw 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 a specific verb ('Search'), the resource ('exercises'), and the method ('by name or keyword with fuzzy matching'). This distinguishes it from sibling tools like get_exercise_by_id or get_exercises_by_body_part, which imply exact or filtered retrieval rather than fuzzy keyword search.

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

Usage Guidelines3/5

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

The description implies the tool should be used when a fuzzy name/keyword search is needed, but it gives no explicit guidance about when to prefer it over sibling tools. It does not state exclusions or alternatives, so the usage context is inferred rather than spelled out.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nitinchakravarthy/workout_tracker_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server