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Glama

Nuget

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?

Annotations indicate write operation (readOnlyHint=false) and idempotent (idempotentHint=true). Description adds key-value store, scoping by identifier, and retention policy (persistent vs 24h), which is valuable context beyond 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?

Three sentences, front-loaded with main purpose, then usage context, then technical details. No filler or repetition.

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 simple schema (2 required strings, no output schema) and clear annotations, the description fully covers storage behavior, pairing with siblings, and retention policy. Adequate for effective use.

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 (key and value). Description supplements with concrete examples of valid keys (subject_property, target_ticker) and values (findings, addresses), adding practical guidance.

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 'Save data the agent will need to reuse later' with specific examples like tickers, addresses, and preferences. It distinguishes from siblings recall and forget by mentioning pairing.

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?

Provides clear when-to-use guidance: 'when you discover something worth carrying forward.' Explains persistence differences (authenticated vs anonymous) but lacks explicit when-not-to-use or alternatives beyond sibling mentions.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same function, ask_pipeworx_grounded is the same router with a different response mode, and polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk all overlap on prediction-market edge detection. search vs search_within vs discover_tools also blur discovery boundaries. An agent would struggle to pick the right tool without reading every long description.

Naming Consistency3/5

All names are snake_case and individually readable, so there's no chaotic style mixing. However, the pattern is inconsistent: bare verbs (search, recall, forget, subscribe), verb_noun (get_package, resolve_entity, scan_dependency), noun phrases (latest_version, recent_alerts), and compound prefixes (pipeworx_*, polymarket_*). The server is named 'Nuget' but the vast majority of tools carry pipeworx_ or polymarket_ prefixes, making the namespace feel like a grab-bag.

Tool Count2/5

35 tools is far too many for a server ostensibly named 'Nuget' — only ~5 tools relate to NuGet package lookup (search, get_package, list_versions, latest_version, scan_dependency), and even scan_dependency is npm-only. The remaining ~30 tools belong to an unrelated Pipeworx research/markets/memory platform. The count is inflated by redundant variants (ask_pipeworx trio, six polymarket tools) rather than distinct functionality.

Completeness2/5

Judged by the server's stated purpose (NuGet), the surface is thin and has dead ends: search and version metadata are covered, but there's no package owner/publisher info, no readme/description body fetch, no download stats beyond totals, and scan_dependency targets the wrong ecosystem (npm). Judged by the actual dominant domain (Pipeworx), coverage is excessive and sprawling. The tool set fails to deliver a coherent, complete surface for either apparent purpose.

Resources