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Glama

Data Baltimore

by io.github.pipeworx-io

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

A4.3/5.0
Behavior4/5

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

Annotations (readOnlyHint=true, destructiveHint=false) are consistent. Description adds scoping to agent identifier and pairing info, which goes beyond annotations without contradicting them.

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 packed with purpose, use cases, scoping, and pairing info. No unnecessary words. Highly concise.

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 read-only tool with one optional parameter and no output schema, the description covers all necessary aspects: purpose, usage, scope, and related tools. No gaps.

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 the description adds no new meaning—only reiterates that omitting key lists all keys, which is already in the schema description. Baseline score 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 it retrieves a value saved via remember or lists all saved keys when key is omitted. It distinguishes itself from sibling tools remember and forget, and uses specific verb+resource language.

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?

Explicitly describes when to use (look up stored context without re-deriving) and pairs with remember and forget. Could be more explicit about alternatives among siblings, but provides clear context.

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

A4.1/5.0
Disambiguation2/5

Several tool groups overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same catalog; entity_profile, recent_changes, and compare_entities cover overlapping company-data territory; ai_visibility_check and scan_competitor_ai_presence are near-duplicates. Agents would frequently need to read long descriptions to pick the right tool.

Naming Consistency4/5

All tool names use snake_case and mostly follow verb_noun patterns (ask_pipeworx, compare_entities, resolve_entity, validate_claim). Minor inconsistencies exist: generic noun-only names like entity_profile, recent_changes, and bet_research, plus inconsistent prefixes (ask_, baltimore_, polymarket_, pipeworx_, scan_) that group by domain rather than action.

Tool Count3/5

34 tools is on the heavy side for a data-access server, though the scope is broad. Several tools feel tangential to the core data mission (remember/recall/forget, generate_llms_txt, pipeworx_feedback, pipeworx_trending), and the ask_pipeworx family plus the six polymarket_* tools inflate the count with overlapping functionality.

Completeness4/5

The surface covers the domain well: discovery (discover_tools, suggest_questions, baltimore_layers), lookup (ask_pipeworx, baltimore_query/recent), grounded verification (ask_pipeworx_grounded, validate_claim), comparison (compare_entities), profiling (entity_profile), change tracking (recent_changes), and prediction-market analysis. Minor gaps include no direct web search and no Baltimore-specific export/bulk operations, but agents can work around these.

Resources