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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

A4.9/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true and destructiveHint=false, and the description confirms it is a write operation. It adds context about persistent memory for authenticated users and 24-hour retention for anonymous sessions, exceeding what annotations provide.

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?

Four sentences, all front-loaded with the primary purpose. Each sentence adds essential information without redundancy or fluff.

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?

No output schema needed; the description covers purpose, usage, scope, persistence, and sibling relations. It fully serves as a standalone guide for a simple write tool.

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?

Schema descriptions cover both parameters fully (100% coverage). The description enhances with example keys and clarifies that value is any text, adding slight additional meaning.

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 saves data for reuse across conversations or sessions. It provides concrete examples (resolved ticker, target address, user preference) and explicitly distinguishes from siblings recall and forget.

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 specifies when to use ('when you discover something worth carrying forward') and how it pairs with recall and forget. It also explains scope by identifier and persistence differences for authenticated vs. anonymous users.

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
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (beta is currently exactly the same as stable), and the Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, bet_research) all scan or price prediction-market opportunities with fuzzy boundaries. Other clusters like memory and subscriptions are clear, but enough overlap remains that an agent can easily misroute a query.

Naming Consistency3/5

Most names are readable snake_case and many follow a verb_noun shape (list_art_crimes, resolve_entity, validate_claim), but the set also contains noun-phrase names (entity_profile, recent_alerts, pipeworx_feedback, deep_research) and brand-prefixed composites (polymarket_kalshi_spread, ask_pipeworx_beta). This is mixed but still scannable; there is no outright convention chaos.

Tool Count2/5

33 tools exceeds the 25+ threshold and is bloated for a server whose name promises FBI art crimes—only two tools relate to that name. Even treated as a Pipeworx platform, the sprawl makes the tool surface harder to navigate than necessary.

Completeness2/5

For the nominal art-crime domain, only list_art_crimes and get_art_crime exist, with no category search, statistics, or art-crime alerting, so an agent expecting art-crime workflows hits dead ends. The unrelated Pipeworx functionality is broadly covered, but that does not make the set complete for its stated server purpose.