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Glama

Data Michigan

by io.github.pipeworx-io

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. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Adds important behavioral context beyond annotations: authenticated users get persistent memory, anonymous sessions expire in 24 hours. 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?

Four sentences with no wasted words: front-loaded purpose, then usage, then storage details. Efficiently conveys all essential information.

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?

With no output schema, the description doesn't mention return values, but for a simple write operation this is acceptable. Covers key use cases and persistence details sufficiently for an agent.

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 already describes both parameters well. Description adds concrete key examples (subject_property, target_ticker) and clarifies value is any text, providing marginal extra value.

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 reuse across conversations/sessions, using a key-value pair scoped by identifier. It distinguishes this from sibling tools recall 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?

Explicitly tells when to use: when agent discovers something worth carrying forward to avoid re-lookup. Also mentions pairing with recall and forget for lifecycle management.

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

There are multiple severe overlap clusters. ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,578 tools, and ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly' — a direct ambiguity. The six polymarket_* tools plus bet_research form another dense, hard-to-distinguish cluster, and entity_profile/recent_changes/compare_entities/resolve_entity all have overlapping entity-investigation purposes. The long descriptions help but an agent would frequently misselect.

Naming Consistency3/5

The dominant families are internally consistent (polymarket_* prefix, ask_pipeworx_* suffix family, and the verb-based remember/recall/forget), which aids navigation. However, the overall set mixes several conventions: single-word nouns (query, datasets, metadata, recall), verb_noun compounds (validate_claim, generate_llms_txt), and domain_noun names (entity_profile, polymarket_edges). Readable, but there is no unified pattern across the server.

Tool Count2/5

34 tools is clearly over the 25-threshold for heaviness, and several earn little distinct value: ask_pipeworx_beta is a live duplicate of ask_pipeworx, the five-algorithm Polymarket family could be consolidated, and meta/utility tools (suggest_questions, discover_tools, pipeworx_trending, generate_llms_txt, scan_dependency) feel bolted on rather than essential. The breadth of the data domain justifies some size, but the redundancy and tangents push it into bloat.

Completeness3/5

Within its core sub-domains the surface is fairly complete: company research has resolve→profile/compare→recent_changes→validate_claim as a full lifecycle, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. The Polymarket workflow is especially thorough (detect→verify→fill-risk→track-decay). However, the server's stated identity ('Data Michigan') is barely served — the Michigan Open Data surface is only search/schema/query with no update or write path — and the scatter of unrelated tools (npm dependency scan, llms.txt generation) makes the overall purpose incoherent, so gaps are hard to evaluate.

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