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Glama

Dblp

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

A4.5/5.0
Behavior4/5

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

With annotations already indicating idempotentHint and destructiveHint, the description adds useful context about scoping by identifier and persistence duration for authenticated vs anonymous sessions, going beyond what annotations provide.

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 (3-4 sentences), front-loaded with the primary purpose, and every sentence adds essential information without 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 tool's simplicity and full schema/annotation coverage, the description sufficiently covers purpose, usage, scoping, persistence, and sibling pairing, making it complete for an agent to use 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 descriptions for both parameters. The description adds value by providing concrete examples of keys (e.g., 'subject_property') and clarifying that value can be any text.

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, specifies it is a key-value store scoped by identifier, and distinguishes from siblings by mentioning 'pair with recall to retrieve later, forget to delete.'

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 explicit guidance on when to use ('when you discover something worth carrying forward') and gives concrete examples, but does not explicitly state when not to use the tool.

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
Disambiguation3/5

Many tools overlap in general purpose—ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all retrieve factual data—though their descriptions draw clear mode distinctions. The Polymarket family is similarly dense but each member has a distinct role. An agent must read carefully to pick the right one, but the boundaries are mostly decipherable.

Naming Consistency4/5

Tool names overwhelmingly follow snake_case verb_noun or domain_noun patterns (search_publications, resolve_entity, polymarket_edges, ask_pipeworx). Minor exceptions like the bare verbs recall and forget break the pattern slightly, and mixed prefixes (pipeworx_, ask_, search_) are still predictable.

Tool Count2/5

At 34 tools, the set is heavy, but the deeper problem is scope mismatch: the server is named Dblp yet only 3 of 34 tools (search_authors, search_publications, search_venues) relate to DBLP. The remaining 31 tools form a broad general-purpose data platform that dwarfs and obscures the apparent purpose.

Completeness2/5

For a DBLP server, the surface is minimal: only search operations exist, with no record fetch-by-id, citation metrics, or author profile detail beyond what search returns. The bulk of the toolset addresses unrelated domains, leaving the actual DBLP workflow thin and incomplete.

Resources