Skip to main content
Glama

Sefaria

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

A4.9/5.0
Behavior5/5

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

Annotations declare idempotentHint=true and destructiveHint=false, which is consistent with 'save' operation. Description adds valuable behavioral details: key-value store, scoping by identifier, persistence differences for authenticated vs anonymous users (24-hour retention). No contradiction.

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?

Description packs all essential information into a single, well-structured paragraph without redundancy or fluff. Front-loads purpose and usage, then adds behavioral details, making it easy to scan.

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?

For a simple 2-parameter tool with no output schema, the description provides complete guidance: purpose, usage scenario, persistence behavior, and sibling tools. Agent has all needed context to invoke 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 descriptive parameter descriptions. Description adds user-meaningful context by providing example key patterns (e.g., 'subject_property') and value types ('findings, addresses, preferences, notes'), 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?

Description clearly states verb and resource ('Save data'), specifies reuse context across sessions, and lists concrete examples (ticker, address, preference). Distinguishes from siblings recall (retrieve) and forget (delete) implicitly by naming them as pairs.

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 states when to use ('when you discover something worth carrying forward') and mentions alternative tools (recall, forget) with explicit pairing instructions. Also provides context for authentication and session duration.

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

All Sefaria toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges). The Sefaria-specific tools are few, making it hard to distinguish between the core functionality and auxiliary utilities.

Naming Consistency2/5

Tool names follow inconsistent conventions: some use verb_noun (ask_pipeworx, get_text), some noun_verb (ai_visibility_check, bet_research), and some are standalone names (pipeworx_feedback, polymarket_edges). This mix of patterns makes the set appear haphazard.

Tool Count1/5

With 33 tools, the server is heavily overloaded, especially given the name 'Sefaria' which implies a focused set for Jewish text access. The vast majority of tools are unrelated to Sefaria, belonging to the Pipeworx ecosystem, making the count inappropriate for the server's stated purpose.

Completeness1/5

For a server named Sefaria, the tool surface is severely incomplete. Only three tools (get_text, get_commentaries, lookup_ref) directly relate to Jewish texts, missing fundamental operations like search, list books, or manage content. The remaining 30 tools belong to other domains, leaving the core domain under-served.

Resources