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Remember

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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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (write, idempotent, non-destructive), the description adds key behavioral details: 'scoped by your identifier' and the persistence distinction between authenticated (persistent) and anonymous (24h) sessions. This context is not present in the annotations and meaningfully informs an agent about side effects and lifetime.

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 that front-load purpose and usage, then detail storage behavior and related tools. Every sentence carries necessary information with no filler, making it both concise and well-structured.

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 tool with only 2 parameters and no output schema, the description covers purpose, when to use it, scoping, persistence, and pairing with related tools. Combined with the fully descriptive schema and adequate annotations, the agent has complete guidance for correct selection and invocation.

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 already covers both parameters with clear descriptions and examples, so the baseline is 3. The description only refers to 'key-value pair' without adding new semantic nuance, though it does reinforce the storage model already implied by the 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 opens with 'Save data the agent will need to reuse later', a specific verb+resource statement that clearly defines the tool's purpose. It distinguishes itself from siblings by mentioning 'Pair with recall to retrieve later, forget to delete', and provides concrete examples like tickers and preferences, leaving no ambiguity.

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 says 'Use when you discover something worth carrying forward' and gives specific scenarios, making the triggering conditions clear. It also references alternating tools (recall, forget), effectively covering usage and alternatives in one concise sentence.

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

A3.6/5.0
Disambiguation2/5

The set contains many overlapping research and prediction-market tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, polymarket_arbitrage, polymarket_edges, etc.) whose boundaries are hard to distinguish despite long descriptions. The PDL enrich tools and memory tools are clear, but an agent would frequently struggle to choose the right query or market-scanning tool.

Naming Consistency2/5

Naming conventions are mixed: there are consistent prefixes like pdl_ and polymarket_, but also arbitrary noun phrases like entity_profile, recent_changes, and bare verbs like remember, forget, and subscribe. There is no consistent verb_noun or action_resource pattern across the toolset.

Tool Count2/5

33 tools is above the recommended range and spans several unrelated domains: PDL enrichment, Pipeworx data lookup, prediction markets, memory, subscriptions, and npm dependency scanning. This feels like several servers merged together rather than a well-scoped toolset.

Completeness1/5

For a server named Peopledatalabs, the PDL surface is severely incomplete: only pdl_person_enrich and pdl_company_enrich are provided, with no PDL search, identify, or list tools. The vast majority of tools are unrelated to PDL, so an agent expecting reasonable PDL API coverage would hit dead ends immediately.