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Glama

Data Sncf

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)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true and destructiveHint=false. Description adds scoping detail ('Scoped to your identifier') and lifecycle pairing, which adds value 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?

Three sentences, front-loaded with the main action. Every sentence adds value without redundancy.

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 read-only retrieval tool with one optional parameter and no output schema, the description covers use case, scoping, and lifecycle pairing comprehensively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a single optional key parameter. Description reinforces the schema's description (e.g., 'omit the key argument'). Baseline 3 is appropriate; no additional parameter semantics beyond 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?

Clearly specifies verb ('Retrieve a value previously saved via remember, or list all saved keys') and resource (saved values/keys). Distinguishes from siblings like remember and forget.

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?

States when to use: 'to look up context the agent stored earlier'. Mentions pairing with remember/forget. Lacks explicit when-not-to-use or alternative tools, but context is clear.

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

All Data Sncf toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes, particularly the ask_pipeworx variants (standard, beta, grounded) and the Polymarket analysis tools (bet_research, polymarket_edges, polymarket_arbitrage). While descriptions help differentiate, an agent may still select the wrong tool for a given task.

Naming Consistency3/5

Names mix verb-initial (ask_pipeworx, compare_entities) and noun-initial (dataset_info, pipeworx_feedback, polymarket_arbitrage) patterns. The snake_case convention is consistent, but the lack of a uniform verb_noun pattern reduces predictability.

Tool Count3/5

With 34 tools, the server is overloaded relative to a clear scope. Many tools are meta-tools (memory, subscription management, feedback) that inflate the count. A more focused set of 15-20 tools would be more coherent.

Completeness4/5

The tool surface covers a wide range of domains: structured data queries, entity profiles, comparisons, prediction market analysis, memory, subscriptions, and SNCF-specific data. Minor gaps exist (e.g., no dedicated weather or sports tools), but the universal ask_pipeworx compensates. Overall, users can accomplish most tasks without dead ends.

Resources