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Jsonschema

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

A4.5/5.0
Behavior4/5

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

Beyond annotations, the description discloses key behavioral traits: storage as a key-value pair scoped by identifier, and persistence details (persistent for authenticated users, 24-hour retention for anonymous sessions). This adds useful context about memory lifecycle that annotations don't cover.

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, front-loaded with the main action, and every sentence provides useful information. It avoids redundancy and is appropriately sized for a simple two-parameter tool.

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 schema and no output schema, the description fully covers the tool's behavior, persistence, and relationship to sibling tools. It answers likely agent questions about memory scope and duration, making it complete for this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes both parameters (key and value) with examples and 100% coverage. The description adds the context of a scoped key-value store, but doesn't significantly enhance parameter-specific meaning beyond what the schema provides, so baseline 3 is appropriate.

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, with a specific verb ('Save') and resource ('data the agent will need to reuse later'). It distinguishes itself from sibling tools by explicitly mentioning companion tools recall and forget, and provides concrete examples of what to store.

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?

The description provides explicit guidance on when to use: 'Use when you discover something worth carrying forward' with examples. It also tells the agent to pair with recall and forget, giving clear context for integration with other tools.

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

All Jsonschema toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Several tools occupy the same entry-point role (ask_pipeworx, ask_pipeworx_beta, deep_research, discover_tools, suggest_questions), and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The Polymarket family is carefully described but still has heavily overlapping discovery/edge surfaces. Only validate_json_schema is unambiguous, and it has no related siblings to clarify its position.

Naming Consistency2/5

Tool names mix verb-first forms (discover_tools, generate_llms_txt, resolve_entity), noun-first forms (entity_profile, pipeworx_feedback), and bare verbs (remember, recall, forget). Prefix conventions are inconsistent—ask_pipeworx, pipeworx_feedback, polymarket_edges, recent_changes—and almost none of the names reflect the server name Jsonschema.

Tool Count1/5

32 tools is already too many, but for a server named Jsonschema only one tool belongs to that domain; the rest form a broad data-research and prediction-market suite. This is an extreme scoping mismatch: the set is simultaneously oversized and almost entirely off-purpose.

Completeness1/5

For a JSON Schema server, the surface is essentially one operation: validate_json_schema. Common schema lifecycle operations—generation, parsing, conversion, ref resolution, linting—are missing, leaving most JSON Schema tasks impossible. The unrelated data-research tools are internally rich, but they do not complete the apparent domain.

Resources