Skip to main content
Glama

Hathitrust

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

A4.9/5.0
Behavior5/5

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

Annotations already provide idempotentHint and destructiveHint; description adds key behavioral context: persistence duration (24 hours for anonymous, permanent for authenticated) and scoping by identifier.

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?

Concise and front-loaded: states purpose, then usage guidance, then behavioral details. Every sentence adds value with no redundancy.

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?

Given the simple input schema and no output schema, the description fully covers what the tool does, when to use, and how it behaves, making it complete for an AI 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 covers both parameters with descriptions; description adds context about scoping and examples for keys, though schema already has examples.

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 later reuse across conversations/sessions, using specific verbs and distinguishing from sibling tools like 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 discovering something worth carrying forward) and provides examples, plus directs to pair with recall and forget.

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

All Hathitrust toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The tool set mixes tools from multiple unrelated domains (HathiTrust, Pipeworx data platform, Polymarket, npm, etc.), making it unclear which tool to use for a given task. Overlap exists between ask_pipeworx and ask_pipeworx_grounded, and between several Polymarket tools, while the HathiTrust-specific tools are few and buried among many others, causing confusion.

Naming Consistency2/5

Naming conventions are inconsistent across the tool set. Some tools use verb_noun (check_full_view, lookup_by_identifier), others use noun_verb (entity_profile, deep_research), and some are single verbs (forget, remember, subscribe). There is no predictable pattern, making it harder for an agent to guess tool names.

Tool Count1/5

With 33 tools, the count is high, but only 3 (get_record, lookup_by_identifier, check_full_view) are relevant to the server's stated purpose (HathiTrust). The remaining 30 tools appear to be from unrelated domains (Pipeworx data platform, Polymarket, npm, etc.), making the tool count severely mismatched with the server name.

Completeness2/5

For the HathiTrust domain, the tool surface is severely incomplete: only three basic lookup tools with no search, browse, or management capabilities. The broader set includes tools for many other domains, but that does not compensate for the lack of coverage of the server's primary purpose.

Resources