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Glama

Doffin

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

A4.6/5.0
Behavior4/5

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

Beyond annotations (idempotentHint, destructiveHint), description adds persistence details (authenticated vs anonymous) and scoping. Minor missing: no explicit statement about overwrite, but idempotentHint covers it.

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, front-loaded with purpose, no wasted words. Efficient and clear.

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?

Lacks return value description (no output schema), but for a simple store, the behavior is well-covered. Could mention confirmation, but not essential.

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%, but description adds meaningful examples for keys and values (e.g., 'subject_property', 'target_ticker'), enhancing understanding beyond 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 saves data for later reuse, gives specific examples (ticker, address, preference, subject), and distinguishes from sibling tools (recall, 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 says when to use (discover something worth carrying forward), pairs with recall/forget, and notes scoping by identifier and retention policies.

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

All Doffin toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Several tool clusters have fuzzy boundaries: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and ask_pipeworx/deep_research/validate_claim overlap for factual research. Polymarket_arbitrage, polymarket_edges, and bet_research also all surface betting opportunities, while ai_visibility_check and scan_competitor_ai_presence serve nearly the same purpose.

Naming Consistency3/5

Names are readable and mostly snake_case, but the pattern is mixed: doffin_* and polymarket_* use domain-prefixed nouns, ask_pipeworx* uses verb+product, and remember/recall/forget/subscribe are bare verbs. There is no single predictable verb_noun convention, though each internal cluster is somewhat consistent.

Tool Count2/5

34 tools is heavy for a server named Doffin, and only three tools actually relate to Norwegian procurement. The remaining surface is mostly general Pipeworx data access, prediction-market analysis, and memory utilities, which feels like several servers bundled under one misleading name.

Completeness3/5

For Doffin read-only access, search + recent + notice detail covers the core workflow well. However, the server's actual domain is fragmented across procurement, Pipeworx research, prediction markets, memory, and subscriptions, which makes coverage difficult to reason about and leaves minor gaps such as no subscription update capability.

Resources