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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

A4.6/5.0
Behavior5/5

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

Annotations already indicate the tool is not read-only (readOnlyHint=false) and not destructive. The description adds valuable behavioral context: memory is scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with a first sentence that captures the core purpose, followed by usage guidance and retention details. It is concise but could slightly trim examples without loss.

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?

Given no output schema, the description fully covers the tool's behavior, including scope, persistence, and pairing with siblings. It provides complete context for an agent to use 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 has 100% coverage with descriptions for key and value. The description adds some examples (e.g., 'subject_property') but does not significantly enhance meaning beyond the schema. A score of 3 is appropriate as the description does not compensate beyond schema.

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 identifies the tool's purpose: saving data for reuse across sessions or conversations. It uses specific verbs ('save data the agent will need to reuse') and provides concrete examples (resolved ticker, target address) that distinguish it from sibling tools like 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 explicitly states when to use the tool ('when you discover something worth carrying forward') and mentions alternatives: 'Pair with recall to retrieve later, forget to delete.' This provides clear contextual guidance.

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

A3.8/5.0
Disambiguation2/5

Multiple tools are near-duplicates or strongly overlapping: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are only mode/version variants, and discover_tools overlaps with suggest_questions, ai_visibility_check with scan_competitor_ai_presence, and the Polymarket tools with each other. An agent would frequently need a deep read of the descriptions to know which one is truly appropriate.

Naming Consistency4/5

The naming is almost entirely snake_case and mostly follows a verb_noun or domain_noun pattern, e.g. query_layer, list_subscriptions, resolve_entity, compare_entities. Minor deviations like entity_profile, pipeworx_feedback, and polymarket_arbitrage are noun-first, but the overall pattern is still recognizable and predictable.

Tool Count2/5

34 tools for a server branded 'Arcgis Tallahassee' is far too many, especially since only a handful of them are GIS-related. The rest constitute a large general-purpose Pipeworx data platform, which at this tool count becomes unwieldy and will increase an agent's selection failure rate.

Completeness3/5

The core read-only GIS flow is covered (search_datasets → layer_info → query_layer), and the Pipeworx side is broad. However, there are notable gaps: no ArcGIS service management, no layer/feature editing, no spatial operations, and no deeper GIS functions. Because the server's stated purpose is ArcGIS-focused, the overall surface is only partially complete relative to that domain.