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Remember

remember
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 and non-destructive behavior. The description adds valuable context beyond annotations: key-value scoping by identifier, persistent memory for authenticated users, and 24-hour retention for anonymous sessions. It does not detail overwrite semantics, but the idempotent annotation covers repeated writes.

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 five tightly focused sentences: purpose, when to use, storage model, retention policy, and companion tools. Every sentence adds a distinct piece of actionable information with no filler or repetition.

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 two-parameter set operation with no output schema, the description is remarkably complete. It covers the operation's purpose, triggers, scope, persistence behavior, and how to retrieve or delete data. No critical usage context is missing.

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%, and both parameters have descriptive schema text. The tool description reinforces the key-value model and gives examples of what to store, but these examples largely mirror the schema's own examples ('target_ticker', 'user_preference'). No additional parameter-level meaning is added beyond 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 and resource — and clarifies scope with 'across this conversation or across sessions.' It also distinguishes itself from siblings recall and forget by positioning remember as the write operation for persistence.

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?

It explicitly states when to use the tool: 'Use when you discover something worth carrying forward' and gives concrete examples like ticker, address, or preference. It also names companion tools: 'Pair with recall to retrieve later, forget to delete,' providing clear context versus alternatives.

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 nearly identical in purpose, and structure/summary both fetch PDB entries. The server name 'Rcsb Pdb' doesn't match most tools, which are Pipeworx data tools, compounding ambiguity.

Naming Consistency3/5

Mostly snake_case verb_noun, but verbs are inconsistent (ask, discover, generate, list, recall) and some names are noun phrases (entity_profile, polymarket_edges). No clear pattern unifies the set.

Tool Count2/5

37 tools is excessive for a server ostensibly about RCSB PDB; only 6 tools relate to PDB while 31 serve unrelated Pipeworx functionality. The count feels like a bundled grab-bag rather than a focused toolset.

Completeness3/5

The PDB-specific tools cover the core operations (search, fetch, assembly, ligand, polymer entity), so the structural biology surface is mostly complete. However, the server's overall purpose is muddled, and the Pipeworx tools are a separate domain that happens to be bundled in, making it unclear what 'completeness' even means for this server.