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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate idempotency and non-destructiveness, but the description adds valuable behavioral context: storage as a key-value pair scoped by identifier, and persistence differences between authenticated users (persistent) and anonymous sessions (24 hours). This goes beyond annotations and does not contradict them.

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 and efficiently covers purpose, usage, scoping, persistence, and sibling relationships. It is front-loaded with the action ('Save data') and contains no filler or redundant restatement of the title.

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?

For a simple two-parameter tool with no output schema, the description is quite complete: it explains purpose, when to use, storage behavior, and related tools. It does not describe the return value, but the tool's simplicity and annotations make this a minor gap rather than a critical omission.

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?

The schema already provides 100% coverage for the two parameters with descriptions and examples. The description adds conceptual meaning by framing the parameters as a key-value pair scoped by the agent's identifier, which helps the agent understand how to structure keys and values beyond the schema's basic definitions.

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 uses the specific verb 'Save' and clearly identifies the resource (data for later reuse), immediately making the tool's function obvious. It also differentiates from siblings by explicitly referencing recall and forget, stating 'Pair with recall to retrieve later, forget to delete,' which distinguishes it from those alternatives.

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 explicit usage guidance: 'Use when you discover something worth carrying forward' with concrete examples (ticker, address, preference). It also names related tools for retrieval and deletion, giving the agent alternatives. However, it does not state when not to use the tool, so it lacks a complete exclusion list.

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

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes, with clear descriptions differentiating similar ones like ask_pipeworx and ask_pipeworx_grounded. A few overlaps exist (multiple Polymarket analysis tools), but descriptions sufficiently resolve ambiguity.

Naming Consistency2/5

Tool naming is inconsistent, mixing descriptive phrases (entity_profile, polymarket_edges) with verb-object patterns (generate_llms_txt, search). No strong convention is followed, and the 'polymarket_' prefix is applied to some betting tools but not others.

Tool Count3/5

32 tools is high but not extreme. However, the scope is too broad for a single server, covering data queries, betting, memory, NYPL, and more, making the set feel bloated and unfocused.

Completeness2/5

The domain is unclear due to mixed tools, but within the NYPL subset there are clear gaps (only search and item, no CRUD). For the other domains, coverage is uneven and lacks clear lifecycle completeness.