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Stratz

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)

TDQS

A4.6/5.0
Behavior5/5

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

The description adds important behavioral details beyond annotations: data is scoped by identifier, authenticated users get persistent memory, anonymous sessions retain memory for 24 hours. Annotations already state idempotentHint true and destructiveHint false, which are consistent.

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, three sentences, with a clear front-loaded purpose statement, then usage guidance, then technical behavior. No wasted words.

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?

For a write tool with no output schema, the description adequately explains the operation and persistence behavior. It could be improved by explicitly noting that a success confirmation is returned, but it remains complete enough.

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%, and the description provides additional context by giving concrete examples (e.g., 'subject_property', 'target_ticker') and explaining that value can be any text. This adds meaning beyond 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 saves data for reuse across conversations or sessions, using key-value pairs. It distinguishes itself from siblings by mentioning pairing with 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use it (when discovering valuable information to carry forward) and mentions pairing with recall/forget. However, it does not explicitly state when not to use it, which would improve the score.

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

B3.3/5.0
Disambiguation2/5

Several tools have overlapping or intentionally duplicated purposes: ask_pipeworx/ask_pipeworx_beta currently behave identically, and the Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) presents multiple scanners with fuzzy boundaries. The long descriptions help, but the set as a whole is hard to navigate without close reading.

Naming Consistency2/5

Naming is a mix of bare single nouns (hero, match, meta, remember, forget), snake_case verb-first names (ask_pipeworx, compare_entities, generate_llms_txt), and noun-first compounds (pipeworx_feedback, bet_research, scan_competitor_ai_presence). There is no consistent verb_noun or noun-verb convention across the set.

Tool Count2/5

At 43 tools, the server bundles at least five unrelated domains (Dota 2 stats, Pipeworx data querying, Polymarket analytics, memory, subscriptions, AI visibility). That is far too many for a focused MCP server, and the mix makes the surface feel like a grab bag rather than a purpose-built toolkit.

Completeness4/5

Each subdomain individually has solid coverage: Dota 2 has heroes/matches/players/tournaments/meta plus a GraphQL fallback, the data layer has discovery + routing + grounding + validation, and memory/subscriptions have full lifecycle operations. The only real gap is cohesion across domains; within each slice there are no obvious dead ends.

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