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Recall stored memories

recall
Read-onlyIdempotent

Retrieve previously stored memories, optionally filtered by search query and/or project. Call at the start of work on a known project to restore context: why decisions were made, known fixes, preferences. Example — GET https://ainetcafe.com/t/recall?query=<what+to+remember> (needs a workspace/key for durable memory)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 8, up to 20).
queryNoOptional search terms; omit to list the most recent.
projectNoOptional project filter.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
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 valuable context beyond that: it notes the need for 'a workspace/key for durable memory' (auth requirement), provides a concrete example URL, and explains the purpose of retrieved content (decisions, fixes, preferences). This goes beyond the baseline.

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 two sentences plus a compact example, with the main action stated first. Every sentence contributes: purpose, use case, example, and auth requirement. There is 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 read-only retrieval tool, the description covers all essential aspects: what it retrieves, when to use it, how to filter, an example call, and an auth prerequisite. Combined with the complete input schema and output schema, the agent has sufficient information to invoke it correctly.

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?

The input schema provides 100% coverage with descriptions for all three parameters (limit, query, project). The description only mirrors this by mentioning 'filtered by search query and/or project' and gives an example URL placeholder, but adds little beyond what the schema already states. Baseline 3 is appropriate.

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 'Retrieve previously stored memories', which is a specific verb+resource pair that clearly distinguishes this from sibling tools like 'remember' (store) and 'forget' (delete). It also specifies the scope (filtered by query/project), leaving no ambiguity about the tool's function.

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 gives explicit context: 'Call at the start of work on a known project to restore context'. It also describes the optional filters. However, it does not explicitly state when to use an alternative tool (e.g., 'remember' for storing), so it falls short of a 5.

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

A3.6/5.0
Disambiguation3/5

Many tools are clearly distinct, but there are several overlapping groups: PDF extraction (extract_invoices, extract_statement, extract_tables, pdf_to_markdown), table comparison (diff_tables vs reconcile_ledger), and model pricing (list_models vs model_costs). Descriptions help clarify boundaries, but an agent could misselect without careful reading.

Naming Consistency3/5

All names use lowercase snake_case, but the verb-noun pattern is inconsistent. Most tools are verb-first (build_app, clean_table, fetch_page), but several are noun-first (jwt_decode, regex_test, web_search), noun-only (ai_visibility, model_costs), bare verbs (recall, remember), or a full phrase (what_can_you_do). This mixed convention is still readable but not predictable.

Tool Count2/5

With 34 tools, this server exceeds the 25-tool threshold for 'too many'. While the breadth covers many utility domains, the count is heavy and some tools could be consolidated or removed. A more focused set would reduce cognitive load and misselection risk.

Completeness3/5

The utility set covers web, PDF, CSV, model, task, and dev tooling well, but there are notable gaps in resource lifecycles. Apps have build/list/get but no update/delete, and memories support remember/recall but no forget. These missing operations could create dead ends for agents.