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Update a startup

update_startup
Destructive

Update a startup you submitted (API key user matches created_by) or that you have a verified claim on. Pass slug plus only the fields to change. The profile stays on the free listing. An edit does not hide it or turn it into a bill. Costs 1 submit credit. Same validation as submit_startup. Needs a free StartupHub.ai account.

Input Schema

NameRequiredDescriptionDefault
slugYesThe startup's slug (e.g. "anthropic")
hq_cityNo
sectorsNo
for_saleNo
logo_urlNoNew logo URL, server fetches and replaces (≤2MB).
is_hiringNo
one_linerNo
github_urlNo
hq_countryNo
is_raisingNo
careers_urlNo
descriptionNo
press_emailNo
twitter_urlNo
founded_dateNo
linkedin_urlNo
contact_emailNo
media_kit_urlNo
raising_roundNo
pitch_deck_urlNo
raising_targetNo
for_sale_detailsNo
operating_statusNoactive | acquired | shut_down | stealth
partnerships_emailNo
raising_use_of_fundsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior5/5

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

With destructiveHint=true already flagged, the description adds substantial context beyond annotations: it discloses the cost (1 submit credit), that the profile stays on the free listing, and that an edit does not hide the listing or incur a bill. It also notes the account requirement and shared validation, giving an agent everything needed to anticipate side effects.

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 action and authorization, then cost and billing clarifications. Every sentence carries information, though the billing-related statements are slightly repetitive.

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?

For a destructive, 25-parameter update with no output schema, the description covers authorization, cost, billing implications, validation, and account prerequisites well. The remaining gap is field-level semantics, which is partially mitigated by the 'only changed fields' guidance.

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?

Schema coverage is only 12% across 25 params, so the description must compensate. It provides the critical partial-update rule ('Pass slug plus only the fields to change'), which is genuinely useful for a 25-field update, but it leaves the vast majority of fields semantically undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Update a startup') and clarifies the ownership scope (submitted by you or verified claim). It's unmistakable which entity is being mutated, though it doesn't explicitly contrast itself with siblings like update_investor/update_person beyond the different resource.

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?

It gives clear preconditions for use: the API key user must match created_by or you must have a verified claim, and it references submit_startup for the validation contract. It stops short of naming the alternative tools for other operations, but the context for legitimate use is explicit.

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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