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Glama

Medicaid Intelligence

by io.github.pipeworx-io

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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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (idempotentHint, destructiveHint), the description adds critical behavioral context: storage as a key-value pair, scoping by identifier, and differing retention policies for authenticated vs anonymous users (persistent vs 24 hours). 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 concise and front-loaded, starting with the primary action ('Save data the agent will need to reuse later'). Each sentence provides distinct value: use case, storage mechanics, retention policy, and relationship to sibling 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 simple memory-write tool, the description covers all essential operational aspects: when to use, how storage works, persistence details, and how to retrieve/delete. Given the idempotent annotation and lack of output schema, no critical context is missing.

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. The description adds some context by explaining the storage model ('stored as a key-value pair'), but does not add significant parameter-specific details beyond the schema's examples. The examples given in the description mirror those in the schema.

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: 'Save data the agent will need to reuse later'. It differentiates from siblings by explicitly mentioning 'Pair with recall to retrieve later, forget to delete', distinguishing from recall and forget. The verb 'save' plus the resource (data/key-value) is specific.

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 gives explicit use context: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also names alternative tools and their complementary roles, saying 'Pair with recall to retrieve later, forget to delete.'

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

All Medicaid Intelligence toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as broad data-query routers. The Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) cover closely related trading/arbitrage functions with unclear boundaries. Even the Medicaid drug tools (medicaid_drug_state_market, medicaid_drug_trend, medicaid_drug_utilization) differ only subtly. Agents will struggle to choose correctly.

Naming Consistency3/5

Most tools use snake_case and descriptive phrases, but the patterns are inconsistent: some are verb_noun (generate_llms_txt, list_subscriptions), some are noun_heavy (medicaid_drug_state_market, entity_profile), and some are single verbs (forget, recall, remember). Versioned names like ask_pipeworx_beta and ask_pipeworx_grounded add to the mix. No dominant convention emerges.

Tool Count2/5

39 tools is far too many for a coherent set, especially for a server named 'Medicaid Intelligence.' A large portion of the tools (Polymarket arbitrage, npm dependency scanning, AI visibility checks, pipeworx meta-tools) are unrelated to the server's apparent purpose. The count feels bloated and unfocused.

Completeness3/5

For the Medicaid domain, the coverage is reasonable: drug utilization, enrollment, managed care, and plan market data are present. However, the server also tries to cover general data lookup, prediction markets, and entity research, making the overall surface feel scattered. Missing obvious Medicaid operations (e.g., provider data, claims, spending by state) suggest notable gaps if the stated purpose is Medicaid intelligence.

Resources