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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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations, such as scoping to 'anonymous IP, BYO key hash, or account ID' and the dual-mode behavior (single value vs. list all keys).

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 three sentences long, front-loaded with the main action, and every sentence earns its place: definition, use case, scoping/relation to siblings. No fluff or 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 tool with one optional parameter, strong annotations (readOnly, idempotent, non-destructive), and no output schema, the description fully covers purpose, usage context, scoping, and sibling relationships. It explains what is returned (a value or key list) by implication, and the safety profile is already annotated.

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% — the property description 'Memory key to retrieve (omit to list all keys)' already documents the parameter fully. The description adds illustrative examples (ticker, address, notes) but does not change or augment the technical semantics of the parameter.

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 opens with a specific verb and resource: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' This clearly states the dual functionality and distinguishes the tool from its siblings (remember and forget) by explicitly naming them.

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?

It provides explicit when-to-use guidance: '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.' It also identifies alternative tools for other operations: 'Pair with remember to save, forget to delete.'

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

B3.4/5.0
Disambiguation2/5

Multiple tools are near-duplicates: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share nearly identical behavior, and validate_claim overlaps heavily with ask_pipeworx_grounded. ai_visibility_check vs scan_competitor_ai_presence also overlap. dataset/organization vs search_datasets/search_organizations could confuse agents.

Naming Consistency2/5

Naming is inconsistent: snake_case (ask_pipeworx, deep_research, entity_profile), long descriptive names (scan_competitor_ai_presence, polymarket_edge_tracker), and terse single words (dataset, organization). No coherent naming convention across the set.

Tool Count2/5

37 tools is heavy, and most are Pipeworx platform tools (Polymarket analysis, npm dependency scanning, llms.txt generation, memory ops) that are far out of scope for a 'Datagouv Fr' French open-data server. Only ~6 tools (search_datasets, search_organizations, reuses_search, resources, dataset, organization) relate to the server's stated domain.

Completeness3/5

The data.gouv.fr browsing surface is reasonably complete: search datasets, search organizations, list reuses, fetch resources, get dataset/organization by slug. However the server lacks common catalogue operations like downloading a resource from a URL, inspecting dataset metadata details, or community/follow features — plus the bulk of tools target entirely different domains, leaving the actual data.gouv.fr scope thin.