Skip to main content
Glama

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. Added

TDQS

A4.5/5.0
Behavior4/5

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

The description adds valuable behavioral context beyond the annotations: memory is scoped by the agent's identifier, authenticated users get persistent storage, and anonymous sessions retain memory for 24 hours. It doesn't explicitly mention overwrite behavior for existing keys, but the idempotentHint annotation partially covers this, and the described scoping and retention rules are significant additions.

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 compact sentences, front-loaded with the core action ('Save data...'), followed by usage context and storage details. There is zero redundancy—every sentence adds necessary information, and the structure is logical and easy to scan.

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 a 100%-covered schema and informative annotations, the description fully covers the operation, when to use it, storage scope, and retention policy. Since no output schema exists or is needed, the description is sufficiently complete for an agent to 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 schema covers 100% of the parameters, including types and examples for key and value. The description reinforces the key-value nature ('Stored as a key-value pair') and gives examples of values ('findings, addresses, preferences, notes'), but it doesn't add substantive new syntax or format details beyond the schema. With full schema coverage, the baseline of 3 is appropriate.

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 later reuse, using the specific verb 'Save' and resource 'data' (memory). It also distinguishes itself from sibling tools by explicitly naming recall (retrieve) and forget (delete), giving concrete examples of what to store.

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 provides explicit guidance on when to use: 'Use when you discover something worth carrying forward' and gives examples. It also names alternatives with clear pairings: 'Pair with recall to retrieve later, forget to delete,' effectively telling the agent when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation1/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same data sources, and the Polymarket family (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) has blurred boundaries. Agents would struggle to pick the right one without reading every description carefully.

Naming Consistency2/5

Naming is mostly snake_case but follows no consistent verb_noun pattern. Verbs vary widely (ask_, get_, list_, search_, compare_, scan_, validate_, remember, recall, forget, subscribe, unsubscribe, discover, generate, resolve, suggest) and many tools are bare nouns (entity_profile, recent_alerts, polymarket_edges). The inconsistency makes the set feel ad hoc.

Tool Count2/5

34 tools is on the heavy side, and the count is inflated by many near-duplicate data-router and prediction-market tools. The server is named 'iconify' yet only 3 of 34 tools actually relate to icons, indicating poor scoping for the stated purpose.

Completeness2/5

For the icon domain, the surface is minimal (list, search, get) with no create/update/delete. For the broader data/prediction-market domain, there are significant gaps in lifecycle coverage (e.g., subscriptions have create/cancel but no pause/resume, and the memory tools lack namespacing). The mixed focus means no single domain is fully covered.