Skip to main content
Glama

Tribal: Rate Retrieval Quality

tribal_feedback

Rate retrieval sessions as positive or negative to build an organic evaluation dataset, capturing when knowledge meaningfully helped or failed your task.

Instructions

Record a quality signal about a retrieval session. Use this when Tribal's knowledge meaningfully helped (or failed to help) your current task.

This is NOT about rating individual items. Item-level signals are captured through the Supports/Contradicts relationship system during ingest. This is about rating the combination of items returned for a query, assembled in a particular way.

Rate "positive" when: Tribal surfaced knowledge that directly informed your approach, saved you from a known pitfall, or provided context that improved your decision-making.

Rate "negative" when: The query should have found relevant knowledge but didn't, or the returned items were irrelevant or misleading for the task at hand.

Feedback builds an organic eval dataset. Be selective: only rate when the signal is clear. If no trace_id is available from the retrieval response, do not submit feedback rather than fabricating a trace_id. Incomplete feedback is noise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
embedding_profile_idNoThe embedding_profile_id from the tribal_discover response being rated, so the lineage records the profile that produced the results.
explored_anchor_idsNoIDs of items used as anchors in tribal_explore calls during the session.
notesNoOptional reasoning. What was good or what was missing?
query_textYesThe original discovery query that initiated the retrieval session.
ratingYesWas this retrieval session helpful?
returned_item_idsYesIDs of items returned by tribal_discover in the rated session.
trace_idYesTrace ID from the tribal_discover or tribal_explore response being rated.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
feedback_idYesID of the recorded feedback.
Behavior5/5

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

With no annotations provided, the description fully carries the burden. It discloses that feedback builds an eval dataset, that submission should be selective and only when signal is clear, and that incomplete feedback (missing trace_id) should not be submitted. This goes beyond basic requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear lead sentence, followed by explicit guidance. It is slightly long but each sentence is informative. It could be slightly more concise, but overall it is front-loaded and well-organized.

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 tool has 7 parameters (4 required) and no annotations, the description covers all necessary aspects: purpose, usage, parameter roles, and behavioral expectations. It mentions that feedback forms an eval dataset, which is important context. The description is complete for the tool's complexity.

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

Parameters5/5

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

Schema description coverage is 100% (all parameters described). The description adds context beyond the schema, such as explaining the purpose of embedding_profile_id (for lineage), explored_anchor_ids (anchors used), and notes (optional reasoning). It does not simply repeat schema descriptions but enriches them.

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's purpose: 'Record a quality signal about a retrieval session.' It uses specific verbs and resources, and distinguishes itself from sibling tools like tribal_discover and tribal_explore by focusing on the combined retrieval session quality.

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 when-to-use guidance (when Tribal's knowledge helped or failed), when-not-to-use (not for individual items), and criteria for positive/negative ratings. It also warns against fabricating trace_id and advises to be selective, making it clear how to use the tool correctly.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tribal-memory/tribal'

If you have feedback or need assistance with the MCP directory API, please join our Discord server