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Food Feeds

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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 declare idempotentHint=true and readOnlyHint=false. The description adds key behavioral context beyond annotations: 'Stored as a key-value pair scoped by your identifier' and 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' This explains persistence and auth-dependence, which the agent needs to know.

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, front-loaded with the primary action, and every sentence adds value: purpose, usage conditions, and storage behavior with sibling references. No wasted words.

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?

The tool has no output schema, but the description covers the key behavioral aspects: scoping, persistence, and related tools. It does not mention return values or error conditions, but for a simple save operation these are not heavily needed. The description is sufficiently complete for an agent 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?

Schema coverage is 100%, with both 'key' and 'value' already described with examples in the input schema. The description only says 'key-value pair' and does not add further parameter-specific details, so it does not exceed the baseline 3.

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's purpose: 'Save data the agent will need to reuse later — across this conversation or across sessions.' It gives concrete examples (resolved ticker, target address, user preference) and explicitly distinguishes from sibling tools by referencing 'recall' and 'forget' for retrieval and deletion, respectively.

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?

The description provides explicit usage guidance: 'Use when you discover something worth carrying forward...' and names alternatives with 'Pair with recall to retrieve later, forget to delete.' This tells the agent exactly when to use this tool and when to use siblings.

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

Many tools have overlapping functionality (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the set includes both food-specific feeds and general data tools without clear separation. Distinguishing between them, especially for an agent, would be difficult.

Naming Consistency2/5

Tool names follow no consistent pattern: some are snake_case (list_feeds, read_feed), others are lowercase with underscores (ai_visibility_check), and many are multi-word without clear structure (polymarket_arbitrage, scan_dependency). This inconsistency makes it hard to predict tool names.

Tool Count1/5

With 34 tools, the count is high, and most tools are unrelated to the server's stated purpose of 'Food Feeds'. The inclusion of general-purpose Pipeworx tools (e.g., deep_research, entity_profile, polymarket tools) makes the set bloated and unfocused.

Completeness2/5

For the food feeds domain, only three tools (list_feeds, read_feed, fetch_feed) are relevant, which is incomplete. The server lacks tools for searching, subscribing, or managing feeds. The presence of many unrelated tools does not compensate for this gap.