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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

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description doesn't need to restate safety. It adds context beyond annotations: scoping to user identifier, listing behavior when key is omitted, and the pair relationships. However, it doesn't mention any rate limits or performance characteristics, but for a read-only key-value retrieval that's acceptable. Overall, very good transparency given annotation coverage.

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 primary action, then usage advice, then scoping and sibling pairing. No filler or redundant information. Every sentence is necessary and informative.

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 retrieval tool with one optional parameter and no output schema, the description fully covers how to invoke it (with or without key), what it returns (value or list of keys), scoping, and how it fits with related tools. Annotations and schema already provide safety and parameter details. Nothing essential is missing.

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?

Input schema has 100% coverage for the single optional parameter 'key' with a clear description. The tool description adds value by explaining the parameter's role in both retrieval and listing modes, and provides examples of typical key values (ticker, address, notes). This goes beyond the schema's minimal description, despite the schema itself being clear.

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 retrieves a previously saved value or lists all keys. It uses specific verbs ('retrieve', 'list') and specific resource ('memory key'). It distinguishes itself from siblings 'remember' (save) and 'forget' (delete) by naming them directly. Examples like 'user's target ticker, an address, prior research notes' make the purpose concrete.

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 when to use: 'to look up context the agent stored earlier — without re-deriving it from scratch.' It explains the effect of omitting the key (list all saved keys) and pairs with sibling tools for save/delete operations. No ambiguity about when to use this tool versus alternatives.

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

A3.9/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., three ask_pipeworx variants, multiple Polymarket analysis tools, and several entity-focused tools). Agents may struggle to select the correct tool for tasks like querying data or analyzing prediction markets.

Naming Consistency4/5

All tool names use snake_case, and most follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities). A few names like ai_visibility_check are slightly less conventional, but overall the naming is consistent.

Tool Count3/5

With 32 tools covering a broad range of data services, the count is on the high side but still manageable. However, the server name 'Idf Events' is misleading, as only one tool relates to events in Paris.

Completeness4/5

The tool set covers core workflows for the Pipeworx platform: data querying, research, comparisons, subscriptions, memory, and feedback. Minor gaps exist, but most user needs are addressed.