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Store a memory (persists across sessions within your workspace)

remember

Persist a durable memory: an architecture decision, a stable user preference, a verified bug fix, or an important discovery. The free beta provides a bounded per-caller/workspace memory pool; no personal API key is required. Do not store secrets or raw logs. Example — tools/call remember {"content":"Deploy key rotates monthly"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoCategory; default "note".
contentYesThe memory itself, self-contained (≤2000 chars).
projectNoOptional project name to scope recall later.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4/5.0
Behavior4/5

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

Beyond the minimal annotation hints, the description discloses meaningful behavioral context: persistence across sessions, a bounded free-beta memory pool, no personal API key requirement, and a prohibition on storing secrets or raw logs. It does not mention eviction or duplicate handling, but the annotations do not contradict the description.

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 compact, front-loads the core purpose with concrete memory categories, and then adds essential constraints and an illustrative example. Every sentence earns its place, and the JSON example clarifies invocation without unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low tool complexity, a complete input schema, and the presence of an output schema, the description covers what to store, what not to store, persistence guarantees, resource bounds, and authentication context. It does not explicitly point to 'recall' for retrieval, but the description is otherwise sufficient for correct tool selection and invocation.

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 already describes all three parameters with 100% coverage, so the description does not need to restate them. It adds a concrete example and reinforces the self-contained content requirement, but most parameter meaning is carried by the schema itself, which makes the baseline 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific action, 'persist a durable memory,' and enumerates concrete memory types such as architecture decisions, user preferences, and bug fixes. It is obvious this tool stores information for later reuse, but it does not explicitly distinguish itself from the sibling tool 'recall' beyond the implied persist/retrieve contrast.

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 strong when-to-use guidance by listing the categories of memories worth storing, and strong when-not-to-use guidance by warning against secrets and raw logs. However, it never names 'recall' or any other alternative for retrieval, so the routing guidance is implicit rather than explicit.

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
Disambiguation5/5

Every tool has a clearly distinct purpose with detailed descriptions that explicitly differentiate even close pairs like diff_tables vs reconcile_ledger and list_models vs model_costs. No two tools appear to do the same thing, and the what_can_you_do tool further resolves any confusion.

Naming Consistency3/5

The majority of tools follow a verb_noun snake_case pattern (build_app, fetch_page, list_tasks), but several notable deviations exist: ai_visibility, china_reachability, model_costs, json_yaml, pdf_to_markdown, what_can_you_do, recall, remember, and jwt_decode. This mixed convention, while still readable, is not fully consistent.

Tool Count3/5

With 34 tools, the count is high and exceeds the typical comfortable range for an MCP server. However, the server is a broad AI utility platform covering web, data, LLM, conversion, and scheduling tasks, and each tool appears to serve a distinct purpose with little redundancy, making the large but organized set borderline appropriate for its scope.

Completeness3/5

The tool surface covers a wide array of common workflows (search, fetch, table operations, PDF extraction, model comparisons, task scheduling, memory). However, check_job references deep_research, translate_pdf, and make_slides which are not present in the tool list, and there is no update tool for tasks/apps or a way to delete memories, leaving some user journeys incomplete.

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