GAIP Outcome & Value Agent
Server Details
Builds a task outcome and cost dossier with explicit unknowns.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-06-18
- URL
TDQS
Scored across 1 tool
With only one tool, there is no risk of confusing it with another; its purpose is clearly specified as building a task-level outcome/value dossier.
The sole tool uses a consistent snake_case, verb_noun-style name with a clear gaip_ namespace prefix; no mixed conventions are present.
A single tool is too thin for a server framed as an Outcome & Value Agent; it likely needs separate operations for listing, retrieving, updating, or verifying outcome records.
The surface covers only dossier construction, with no tools for reading, updating, deleting, or managing the underlying tasks, costs, or claims, creating notable lifecycle gaps.
Available Tools
1 toolgaip_measure_outcome_valueMeasure task outcome and valueCInspect
Build a task-level delivery, downstream-use and full-cost dossier. Missing costs and unverified claims remain explicit; no ROI or causal-value claim is issued.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| costs | Yes | ||
| delivery | Yes | ||
| synthetic | No | ||
| episode_id | Yes | ||
| task_class | Yes | ||
| evidence_refs | No | ||
| downstream_use | No | ||
| counterparty_id | Yes | ||
| baseline_cost_gbp | No | ||
| continuity_handle | No | ||
| data_classification | Yes | ||
| independent_operator_id | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations disclose the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false), so the write nature is partly covered. The description adds two genuine behavioral constraints: missing costs and unverified claims are surfaced rather than hidden, and no ROI/causal-value is asserted. It does not, however, explain persistence, the continuity_handle/token mechanism, idempotency, or auth requirements for a mutating tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with no filler, and the core output (the dossier) is front-loaded. It is efficient, though a definition for a 13-parameter nested write operation is arguably too terse to be self-sufficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 13 parameters, nested objects, 7 required fields, no output schema, and a write annotation, the description omits the structural detail an agent needs (required vs optional inputs, the continuity_handle, the data_classification enum). The epistemic-boundary statements are useful but do not make the definition 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% across 13 parameters, including nested objects (task, delivery, costs, downstream_use) and an enum (data_classification), so the description must carry parameter meaning and largely does not. 'Missing costs' faintly gestures at the costs object and 'unverified claims' at evidence_refs, but required fields, the continuity_handle contract, and the classification enum are undocumented anywhere.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The verb is reasonably clear ('Build ... dossier') and the resource scope is named (task-level delivery, downstream-use, full-cost). However 'dossier' is opaque and the description actively narrows the name's promise ('no ROI or causal-value claim is issued'), leaving the agent to infer what artifact is actually produced. No siblings exist to differentiate against, so this is adequate but not sharp.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no when-to-use, when-not-to-use, or prerequisite guidance. The description states what is produced but never says what situation should trigger this tool versus any other action. With no siblings the burden falls entirely on the description, and it is not met.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
gaip_measure_outcome_value
Related MCP Connectors
Read-only machine payment preflight using dated MEO evidence, with explicit unknowns.
Bounded agent exploration with persistent progress, deterministic receipts, and return contracts.
Unpaid qualification-gated operational_context canary. Staging data. No wallet.
Outcome-first agent fallback: free discovery, minimal routing, declared costs, verified execution.
Related MCP Servers
- AlicenseBqualityDmaintenanceVerifiable agent-to-agent task handoff with signed provenance chain.5MIT
- AlicenseCqualityAmaintenancePersistent multi-agent work graph and document-state machine for contracts, specs, slices, evidence, verification, handoffs, and cost-aware AI execution.62Apache 2.0
- AlicenseAqualityAmaintenanceA machine-enforced workflow protocol for AI coding agents that eliminates false progress. Tasks require verifiable evidence to close, while decisions, assumptions, and open questions persist in a versioned reasoning graph for seamless agent handoffs. Designed to keep deep, long-running projects coherent across sessions, agents, and months.3925AGPL 3.0
- AlicenseAqualityBmaintenanceEnables construction teams to create local projects, record sourced field evidence, run deterministic quantity calculations, and draft daily logs, estimates, and change orders with visible assumptions and human approval gates. Keeps all data local and portable with no external side effects.10Apache 2.0
Glama MCP Gateway
Add one secure layer between your agents and this server.