elc-toolkit

Community launch readiness test — should you start a local meetup?

assess_community_launch_readiness

Read-onlyIdempotent

Answers 'should I start an engineering-leadership meetup in my city?' using ELC's own new-city launch playbook (the real doc used to launch Brno, Bratislava and Kraków) as the checklist: speaker network, promo channels, a named local operator, realistic first-event targets, and a promo budget. Call without answers to get the 5 questions; call with all 5 to get a verdict plus the specific open gaps.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`answers`	No	Answers keyed by dimension id, true/false. Valid keys: speaker_network, promo_channels, local_operator, realistic_targets, promo_budget. Omit to receive the 5 questions first.
`context`	No	Optional. A short description of your goal and why you are calling this tool. Recorded as intent so the tools can be improved; it never changes the answer.

Output Schema

TableJSON Schema

Name	Required	Description
`report`	Yes	The full human-readable report.
`source`	Yes	Canonical engineeringleaders.io page this answer is derived from.
`verdict`	No	Headline verdict, when the tool returns one.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 11, 2026
- addedInput schema / properties / answers / additionalProperties / anyOf
```
Added value: +[
+  {
+    "type": "boolean"
+  },
+  {
+    "type": "string"
+  }
+]
```
- removedInput schema / properties / answers / additionalProperties / type
```
Removed value: -"boolean"
```

Changed2 schema fields changedSep 11, 2026

changedInput schema / properties / context / description

Previous value: -"Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\""New value: +"Optional. A short description of your goal and why you are calling this tool. Recorded as intent so the tools can be improved; it never changes the answer."

removedInput schema / required
```
Removed value: -[
-  "context"
-]
```

Changed2 schema fields changedAug 27, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
+  "type": "string"
+}

addedInput schema / required
```
Added value: +[
+  "context"
+]
```

First observedAug 4, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is fully covered. The description adds genuinely useful behavioral context beyond that: the two-phase interaction model, and what is returned at each phase (questions vs. verdict plus gaps). It stops slightly short of detailing verdict format or scoring logic.

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?

Front-loaded with the core question, then the basis, then the two call modes. The dimension list is dense but each item is load-bearing. One sentence carries a lot of clauses, but nothing is redundant.

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?

An output schema exists, so return shapes need not be explained, yet the description still tells the agent what each phase yields. With 0 required params, a nested answers object, and full annotation coverage, there is no gap an agent would need to close before calling it.

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%, so the baseline is 3. The description goes further by enumerating the five checklist dimensions (speaker network, promo channels, local operator, realistic targets, promo budget), which map directly onto the answers keys, giving the agent semantic grounding for the nested object beyond the schema's key list.

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?

States a specific question it answers ('should I start an engineering-leadership meetup in my city?') and the resource it draws on (ELC's new-city launch playbook used for Brno, Bratislava, Kraków). This clearly distinguishes it from siblings like benchmark_leadership_ratio or build_partnership_business_case, which are analytical tools, not checklists.

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 prescribes both invocation modes: 'call without answers to get the 5 questions; call with all 5 to get a verdict plus the specific open gaps.' This is a complete when/when-not instruction with a two-phase workflow, leaving nothing to inference.

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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