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Glama

Datagov Il

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds valuable behavioral context: scoping to an identifier, listing all keys when the argument is omitted, and its role in a save/retrieve/delete workflow. It does not contradict 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 three sentences, front-loaded with the main action, and each sentence adds meaningful information (behavior, usage, scoping, related tools). No wasted words.

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 tool with one optional parameter, no output schema, and clear annotations, the description fully covers behavior, scoping, and relationships to remember/forget. It also implies the return behavior (value or list of keys), making it complete for an agent to invoke correctly.

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

Parameters4/5

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

Schema coverage is 100% with a clear property description. The tool description goes beyond the schema by illustrating example key values ('user's target ticker, an address, prior research notes') and explicitly stating that omitting the key lists all keys, enriching the parameter semantics.

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: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' It uses a specific verb ('Retrieve') and resource, and distinguishes from siblings by explicitly naming remember and forget as complementary tools.

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?

The description provides clear usage context: 'Use to look up context the agent stored earlier' and contrasts with re-deriving. It names alternatives ('Pair with remember to save, forget to delete'), though it does not explicitly list when not to use the tool, giving it a 4 rather than 5.

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

All Datagov Il toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying engine and differ mainly in confidence/extraction mode. Similarly, bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities, and scan_competitor_ai_presence is just a wrapper around ai_visibility_check. The detailed descriptions help, but an agent could easily pick the wrong one without reading very carefully.

Naming Consistency3/5

Naming is a mix of verb-first snake_case (list_groups, search_within, resolve_entity, validate_claim) and noun-first patterns (pipeworx_trending, polymarket_arbitrage, dataset_details, entity_profile). There are also inconsistent verb styles (ask_, search_, list_, scan_, get_/fetch_ are absent). Within families like polymarket_* the pattern is consistent, but overall the set does not follow one clear convention.

Tool Count2/5

At 36 tools, the server exceeds the 25-tool threshold for 'too many.' Even though the underlying Pipeworx catalog is vast, the MCP surface exposes far more than a typical agent needs for any single task, and the server name 'Datagov Il' implies a much narrower scope than the prediction-market, memory, and AI-visibility tools suggest.

Completeness4/5

For the apparent scope — authoritative data lookup, entity research, prediction-market analysis, subscription monitoring, and CKAN dataset access — the surface is fairly complete. It covers read/query, grounded verification, deep research, profile building, comparison, entity resolution, memory, and subscription lifecycle. Minor gaps exist (e.g., no CKAN write operations, no batch dataset export tool), but they do not block core workflows.

Resources