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Finance 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)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare idempotentHint=true and destructiveHint=false, but the description adds valuable context: persistence lifetime (24 hours for anonymous, persistent for authenticated), scoping by identifier, and the intended pairing with recall/forget. This goes beyond the annotations without contradicting 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 four sentences, each serving a distinct purpose: core function, when to use, storage behavior, and related tools. It is concise, front-loaded, and every sentence adds value without 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 simple 2-parameter tool with no output schema, the description covers all needed context: purpose, usage triggers, persistence semantics, and sibling relationships. Combined with strong annotations, this is fully complete for an agent to select and invoke the tool 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 clear descriptions and examples for both key and value. The description merely calls them "key-value pair" without adding extra semantics, which matches the baseline for high schema coverage. No additional parameter details are needed.

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 function: "Save data the agent will need to reuse later." It specifies both the verb (save) and the resource (key-value pairs scoped by identifier), and distinguishes it from sibling tools by naming recall and forget as complementary operations.

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?

Explicit usage guidance is provided: "Use when you discover something worth carrying forward... so you don't have to look it up again." It also names alternatives, saying "Pair with recall to retrieve later, forget to delete," giving the agent clear direction on when to use this tool versus its 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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is overlap among query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and several Polymarket-specific tools. Descriptions help differentiate, but some confusion is possible.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, compare_entities, validate_claim). No mixing of conventions, and naming is predictable.

Tool Count3/5

33 tools is a high count for a finance server, including many meta-tools (memory, subscriptions, feedback) that are not finance-specific. The core finance set is reasonable, but the overall surface feels heavy.

Completeness3/5

The server covers many data sources (SEC, FRED, FDA, etc.) but lacks direct stock quotes or fundamental CRUD operations. Some areas (e.g., Polymarket, AI visibility) are over-represented, leaving gaps in core finance tasks.