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mcp-server

by io.github.forcedreamai

Search agent reliability data

forcedream_search_reliability
Read-only

Real, system-measured reliability per agent: success_rate, avg_latency_ms, sample_size. No key needed. Same real data as forcedream_search_agents' health field, exposed standalone for reliability-focused queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_slugNoOptional: filter to one agent slug. Omit to return all.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_slugNo
reliabilityNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds value by confirming it returns 'Real, system-measured' data and listing the specific fields. It also clarifies no key is needed, which is beyond the annotations. No contradictions with annotations.

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 extremely concise at two sentences, with no wasted words. It front-loads the core information (what data is returned) and immediately provides context about its relationship to another tool. Every sentence earns its place.

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?

Given the tool is simple (one optional parameter, no required fields, output schema exists), the description is nearly complete. It mentions the key fields returned and explains the relationship to forcedream_search_agents. The only minor gap is that it doesn't explicitly state that omitting agent_slug returns all agents, but that's covered by the schema description.

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 has 100% description coverage for the single parameter agent_slug, which is described as optional and for filtering. The description does not add any extra meaning or constraints beyond the schema, so it meets the baseline expectation without providing additional semantic value.

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 states the tool returns 'system-measured reliability per agent: success_rate, avg_latency_ms, sample_size', clearly specifying the verb (search/reliability) and resource (agent reliability data). It distinguishes itself from the sibling forcedream_search_agents by noting it exposes the same health field standalone, making the purpose unambiguous.

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 says 'Same real data as forcedream_search_agents' health field, exposed standalone for reliability-focused queries', which guides when to use this tool vs. the sibling. It also mentions 'No key needed', implying ease of access. However, it does not explicitly list when not to use it or provide other alternatives.

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

A4/5.0
Disambiguation3/5

Most tools have clearly distinct purposes (fraud vs extract vs generate vs sentiment vs lead scoring vs quote vs proof verification). However, there is notable overlap among the search_* discovery tools: forcedream_search_agents, forcedream_search_reliability, and forcedream_search_costs all surface overlapping agent metadata (success_rate appears in both search_agents and search_reliability), which could cause misselection. Additionally, forcedream_extract_data vs forcedream_extract_entities vs forcedream_extract_action_items overlap somewhat in the extraction domain despite distinct outputs (JSON fields vs raw entities vs action items).

Naming Consistency4/5

The forcedream_ prefix is used consistently throughout, and most tools follow a forcedream_<verb>_<object> pattern (extract_data, generate_code, score_lead, security_scan). However, there is inconsistency in verb style: check vs extract vs generate vs invoke vs search vs verify vs summarize are all different verb types, and the objects don't follow a uniform noun convention (some are actions like invole_agent, others resources like market_quote). The naming is readable and discoverable but not perfectly uniform.

Tool Count4/5

At 17 tools, this is slightly above the ideal range but justifiable given the broad multi-service scope (fraud, extraction, generation, discovery, verification). Each tool maps to a reasonably distinct service capability, and none feel like padding. The count borders on heavy but earns its place given the diverse domain coverage.

Completeness4/5

The tool surface is comprehensive for a multi-purpose AI/ML service platform, covering fraud detection, data extraction, code generation, sentiment analysis, embeddings, lead scoring, security scanning, summarization, market quotes, agent discovery, and proof verification. Missing are update/delete operations, but this appears to be a stateless service rather than a CRUD resource store. The discovery tools (search_* variants) and meta capabilities (verify_proof) round out the lifecycle well, though there's no clear cleanup or batch-processing tool.

Resources