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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

A4.2/5.0
Behavior4/5

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

Consistent with annotations (readOnly, idempotent, non-destructive) and adds scoping context. Does not mention potential error cases but annotations already cover safety.

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?

Two sentences, fully front-loaded with purpose and usage, no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequately covers purpose, usage, scoping, and pairing. Lacks explicit error conditions but acceptable for a simple retrieval tool with robust annotations.

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%, so baseline is 3. Description adds scoping context but does not significantly extend the schema's parameter description.

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 retrieves a saved value or lists all keys when omitted, explicitly distinguishing from siblings '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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a clear use case ('look up context stored earlier') and scoping information, though it does not explicitly state when not to use it.

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
Disambiguation3/5

While many tools have detailed descriptions that help differentiate them, there is significant overlap among query tools like ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research. The prediction market tools also cluster together, making it challenging for an agent to quickly pick the right one without careful reading.

Naming Consistency4/5

Most tools follow a descriptive snake_case convention (e.g., ask_pipeworx, entity_profile, compare_entities). Minor deviations exist, such as 'ai_visibility_check' and 'deep_research', but overall the naming pattern is predictable and clear.

Tool Count3/5

With 33 tools, the server is larger than typical single-domain servers. While it supports a broad data platform, this count feels somewhat bloated and could benefit from consolidation, especially among overlapping query tools.

Completeness1/5

The server is named 'Materials' but contains only two materials-specific tools (materials_search, materials_stability). The remaining 31 tools cover unrelated domains (finance, economics, prediction markets, etc.), leaving the stated domain severely incomplete.