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Glama

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

The annotations already declare readOnlyHint and idempotentHint, so the description adds valuable context beyond these: scoping to the agent's identifier (anonymous IP, BYO key hash, or account ID) and the optional behavior of listing all keys when the key argument is omitted. It also clarifies the memory lifecycle with remember and forget. 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.

Conciseness5/5

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

The description is two sentences, front-loaded with the primary action and followed by usage context and scope. Every sentence earns its place, with no redundant information or filler.

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 tool with one optional parameter and no output schema, the description is complete: it explains the operation, the parameter behavior, the scoping rules, and the relationship to sibling memory tools. The annotations cover safety, so no further behavioral disclosure is necessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100% with a clear description for the key parameter, the tool description reinforces and expands by explicitly stating 'omit the key argument' to list all keys, adding behavioral meaning beyond the schema definition. This clarifies the optionality and dual-mode functionality.

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's function with a specific verb and resource: 'Retrieve a value previously saved via remember, or list all saved keys'. It also provides concrete examples of stored context (target ticker, address, research notes), making it easy to distinguish from sibling tools like remember 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: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' It also mentions the sibling tools for related operations: 'Pair with remember to save, forget to delete,' effectively providing alternatives and exclusions.

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

Several tools are easy to confuse: ask_pipeworx_beta is currently identical to ask_pipeworx, deep_research overlaps heavily with ask_pipeworx/ask_pipeworx_grounded, and the six polymarket_* tools have closely related purposes. ai_visibility_check and scan_competitor_ai_presence also overlap, making selection error-prone.

Naming Consistency4/5

Almost all tools use lowercase snake_case and mostly follow a verb_noun pattern (query_layer, resolve_entity, scan_dependency, validate_claim). A few noun-style names like entity_profile, layer_info, and pipeworx_trending deviate slightly, but the overall convention is predictable.

Tool Count2/5

34 tools is well past the heavy threshold, and the server is named 'Arcgis Charlotte' while only three tools actually relate to ArcGIS. The rest form a sprawling Pipeworx research, prediction-market, subscription, and memory toolkit, which creates a severe scope mismatch.

Completeness2/5

For the Pipeworx data side the surface is quite thorough, but for the declared ArcGIS Charlotte purpose it is thin: search_datasets, layer_info, and query_layer provide read-only access with no update/delete, analysis, or dataset management. The tool set therefore has a significant gap relative to its stated domain.