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gapup-mcp

by io.github.getgapup

webhooks_manage

Manage HTTP webhook callbacks for async tools (T5/T6 batch flagships). Instead of polling every 5s, register a callback URL — Gapup posts the job result to your endpoint the moment it completes. Supported events: job.completed | job.failed | monitoring.alert | quota.threshold. Modes: register (add endpoint), list (view active webhooks), revoke (soft-delete), test (fire a test payload to verify your receiver), history (last 20 fires). Security: every delivery is signed with HMAC-SHA256 on the body — verify the X-Gapup-Signature header against sha256(secret, body).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo(register) HTTPS/HTTP endpoint that will receive POST callbacks. Must return 2xx within 10s.
modeYesregister — add a webhook endpoint. list — view your active webhooks. revoke — soft-delete a webhook by webhook_id. test — fire a test payload to verify the receiver is alive. history — last 20 delivery attempts for a webhook.
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
eventsNo(register, optional) Events to subscribe to. Defaults to all events if omitted.
secretNo(register, optional) A secret string used to sign deliveries with HMAC-SHA256. Store it safely — verify X-Gapup-Signature header on your receiver.
webhook_idNo(revoke / test / history) The webhook_id returned from register.
caller_hashNoOptional caller identity override. If omitted, uses the internal session hash.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the sparse annotations (readOnly=false, destructive=false), the description discloses important behaviors: soft-delete for revoke, test payload firing, history limited to 20 entries, HMAC-SHA256 signing, and the 10s response requirement. These are not evident from annotations or schema.

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 a single well-organized paragraph that front-loads the core purpose, then covers modes, events, and security in a compact, non-redundant manner. Every sentence adds essential information without padding.

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's complexity (5 modes, event subscriptions, security), the description covers all key usage aspects including authentication, event types, and behavior. With a present output schema, return values are not required in the description, making it 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?

The schema already provides full descriptions for all 7 parameters (100% coverage). The tool description adds value by clarifying the workflow (e.g., register returns webhook_id for later use, async mode returns job_id) and by explaining the security parameter's role, going slightly beyond the schema.

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 it manages HTTP webhook callbacks for async tools, and lists five distinct modes (register, list, revoke, test, history). This specific verb and resource scope distinguishes it from all sibling tools, which focus on other domains.

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 explicitly positions the tool as an alternative to polling ('Instead of polling every 5s'), and explains the purpose of each mode. It also specifies supported events and security requirements, giving clear context for when and how to use the tool.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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