Skip to main content
Glama
Elmoaid

TempoGraph

by Elmoaid
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

learn_recommendation

Get a data-driven strategy recommendation from learned usage patterns. Input a task type for optimal modes, token cost, and success rate, or leave blank for all.

Instructions

Get a data-driven context strategy recommendation from learned usage patterns.

Returns the best modes to use, expected token cost, and success rate for a given task type.
Known task types: debug, feature, refactor, code_navigation, orientation, cleanup,
architecture, dependency_audit, code_review, task_preparation, output_review,
learning, patterns.

Leave task_type empty to see all learned strategies for this repo.

NOTE: Requires the tempo package to be installed. Returns a LEARN_UNAVAILABLE
error if not installed — install with: pip install -e .

output_format: "text" (default) or "json" for structured response

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_pathNo/demo
task_typeNo
output_formatNotext

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.2/5.0
Behavior3/5

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

Discloses dependency on tempo package and the error if missing, but omits details on permissions, side effects, or read-only nature. With no annotations, the description adds moderate behavioral context but is not exhaustive.

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, well-structured, and front-loaded with the core purpose. Every sentence contributes value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the tool's purpose, parameters, usage note, and output options. With an output schema present, it need not detail return values. Minor gaps include not explaining 'best modes' or linking to sibling tools.

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?

Despite 0% schema coverage, the description explains task_type (with list and empty case) and output_format (default and options). Repo_path is not explicitly explained but is self-explanatory from its name and default. Overall, it adds meaningful 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 it returns a data-driven context strategy recommendation, lists specific outputs (best modes, token cost, success rate), and enumerates known task types, making the purpose distinct and unambiguous from sibling tools.

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?

Provides explicit instructions: leaving task_type empty shows all strategies, requires tempo package, and mentions output_format options. However, it lacks explicit differentiation from alternatives like 'suggest_next' or 'get_patterns'.

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

Install Server

Other Tools

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/Elmoaid/TempoGraph'

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