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Glama

Data Rennes

by io.github.pipeworx-io

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds behavioral context: retrieving saved values, scoping per identifier, and the ability to list all keys. No contradictions.

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 extremely concise: three sentences that front-load the core action, then provide use cases and context. Every sentence adds value with no redundancy or filler.

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 tool with one optional parameter, the description covers purpose, usage, scoping, and pairing with sibling tools. It is complete enough given the low complexity and supporting annotations.

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 a clear description of the 'key' parameter. The description reinforces this by explaining that omitting the key lists all saved keys, and adds context about the memory system ('previously saved via remember'). This enhances understanding 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 the tool's primary function: retrieve a specific value by key or list all keys. It uses specific verbs ('retrieve', 'list') and resources ('value', 'keys'), and references sibling tools ('remember', 'forget') to differentiate.

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?

The description provides clear guidance on when to use the tool ('look up context the agent stored earlier') and scoping ('scoped to your identifier'). It implicitly suggests alternatives by mentioning 'remember' and 'forget', but does not explicitly state when not to use it.

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

All Data Rennes toolsInspect
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TDQS

A4.1/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as multiple data query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and several prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). While descriptions attempt to differentiate, an agent could easily select the wrong tool.

Naming Consistency3/5

Tool names use a mix of verb-initial (ask_pipeworx, compare_entities) and noun-phrase patterns (entity_profile, dataset_info), with no consistent verb_noun structure. Naming is readable but lacks a predictable pattern.

Tool Count3/5

With 34 tools, the server is on the heavy side. The broad scope (data querying, prediction markets, company research, local open data) somewhat justifies the count, but several tools could be consolidated (e.g., the various polymarket tools).

Completeness4/5

The tool set covers a wide range of functionalities including data querying, company research, prediction market analysis, and memory management. Minor gaps exist (e.g., no user authentication tools beyond subscriptions), but the surface is largely comprehensive for its intended purpose.

Resources