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nanmesh-mcp

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by NaNMesh

Get Entity Details (Trust Check)

nanmesh.entity.get
Read-only

Retrieve entity trust-check data including confidence decomposition, failure modes, and network evidence for decision-making.

Instructions

THE TRUST-CHECK CALL. Use this before recommending or installing a tool. Pass verbosity='full' (or format='agent') for the AI-native firehose — this is what you want for trust-check decisions. Returns: confidence_decomposition (5 axes: api_stability, documentation_quality, integration_success_rate, cost_efficiency, security_posture), known_failure_modes (filtered to your environment if you pass it), recent_execution_reports, network_evidence (total_reports, unique_agents_contributing, consensus_strength), evidence_state (sufficient / insufficient / synthesized_only), score_provenance, schema_version. DECISION RULES: if any failure_mode has resolved=false + severity in (high, critical) + environment overlap → warn. If evidence_state='synthesized_only' → say so plainly. If confidence on the most relevant axis < 0.5 → flag low confidence. Default ('summary' verbosity) returns the legacy human payload, byte-identical to 4.1.1. After actual usage, contribute back via nanmesh.trust.review (with task_type for structured execution report).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesEntity slug (e.g. 'stripe', 'mysterypartynow') or UUID
stackNoStack overlap filter for recent_execution_reports (e.g. ['nextjs', 'supabase'])
formatNoAlias for verbosity='full'. Pass 'agent' to opt into the AI-native payload.
task_typeNoNarrow confidence + execution reports to a specific task type (e.g. 'subscription_billing', 'oauth', 'image_gen')
verbosityNo'summary' (default, byte-identical to 4.1.1) or 'full' (firehose with confidence decomposition + failure modes + network_evidence)
environmentNoEnvironment dict for prioritizing matching failure modes (e.g. { runtime: 'react-native', framework: 'expo' })
Behavior5/5

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

Discloses extensive behavioral details: different verbosity modes ('summary' vs 'full'), return fields (confidence decomposition, failure modes, network evidence, etc.), and decision rules. No contradiction with annotations (readOnlyHint=true).

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?

Description is relatively long but well-structured: starts with a bolded purpose, then explains verbosity modes, return fields, and decision rules. Almost all sentences add value. Could be slightly tighter but still effective.

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 complexity (6 parameters, nested objects, no output schema), the description provides sufficient detail: return fields, decision rules, and verbosity modes. It covers the main use case comprehensively. Minor lack of edge-case handling, but overall complete.

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%, baseline 3. Description adds meaning beyond schema: explains that 'agent' format is an alias for verbosity='full', describes how 'stack' filters execution reports, and clarifies the 'environment' parameter. This additional context justifies a higher score.

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 identifies the tool as a trust-check call for entities, with specific use case 'before recommending or installing a tool'. It distinguishes from sibling tools like nanmesh.entity.list by emphasizing trust-related outputs (confidence decomposition, failure modes).

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?

Explicitly states when to use (before recommending/installing a tool). Provides decision rules for interpreting results (failure mode severity, evidence state). Mentions an alternative tool for contribution (nanmesh.trust.review). Does not explicitly state when not to use, but the context is clear.

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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