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jon-lind-sn

sn-community-events-mcp

by jon-lind-sn

get_community_events

Retrieve ServiceNow Community events for a given date range, returning structured data with optional filters and CSV export.

Instructions

Fetch ServiceNow Community events (SNUGs, webinars, World Forum stops, etc.) within a date range and return them as structured data.

Args:
    start_date: Inclusive start of the date range, ISO format YYYY-MM-DD.
    end_date: Inclusive end of the date range, ISO format YYYY-MM-DD.
    status_category: Optional filter slug. Only "upcoming" is confirmed to work;
        other values frequently 400 -- if one fails, drop it and retry without it
        rather than guessing an alternate spelling.
    location_category: Optional filter slug, e.g. "in-person", "virtual", "hybrid".
    product_category: Optional filter slug, e.g. "app-engine". The full product
        list is long (from the UI's Product filter) -- pass whatever slug the
        caller gives, or slugify a plain label (lowercase, spaces to hyphens).
    type_category: Optional filter slug, e.g. "webinar", "workshop", "academy",
        "office-hours", "360-exchange".
    include_full_description: If true (default), fetch each matched event's own
        detail page to get the full description instead of the listing's
        "..."-truncated blurb. Costs one extra HTTP request per matched event --
        set to false for date ranges wide enough that this would be slow (a few
        dozen events is quick; a few hundred will take a while, sequentially).
    save_csv_path: If given, also write the results to a CSV file at this path
        (columns: category, title, description, date_text, parsed_date, location,
        url). Embedded line breaks in text fields are rendered as literal `<br>`
        rather than real newlines, so each event stays on one CSV row.

Returns a dict with `events` (list of event dicts) and `meta` (counts: how many
events matched, how many full descriptions were/weren't fetched, etc. -- surface
anything notable from this to the user rather than silently discarding it).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateYes
start_dateYes
save_csv_pathNo
type_categoryNo
status_categoryNo
product_categoryNo
location_categoryNo
include_full_descriptionNo
Behavior5/5

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

With no annotations, the description fully discloses behavior: it fetches detail pages optionally at a cost, writes a CSV when save_csv_path is given, returns specific meta counts, and explains failure modes (status_category 400s). It also notes CSV line-break handling. This far exceeds what annotations would have provided and leaves no safety or side-effect ambiguity.

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 structured cleanly: a one-sentence summary, then a clear Args block with per-parameter explanations. While long, every sentence carries necessary information, especially given 8 parameters and 0% schema coverage. It is front-loaded with the tool's essence and avoids redundancy.

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?

Owing to the absence of an output schema, the description explicitly details the return structure (events list and meta counts) and the CSV output format. It covers edge cases like truncated descriptions, slow performance, and retry logic. The tool has high complexity, and the description addresses all key aspects, making it complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, yet the description meticulously explains every parameter: date formats, filter slug caveats, examples for type_category, the long product list, performance implications for include_full_description, and CSV column details. It adds enormous meaning beyond the bare schema, exactly what this dimension rewards.

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 opens with a specific verb ('Fetch') and a clear resource ('ServiceNow Community events'), enriched with concrete examples (SNUGs, webinars, World Forum stops). It plainly states the tool returns structured data within a date range. Even with no sibling tools to differentiate from, the purpose is unmistakable.

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 provides strong in-tool usage guidance: it explains when to set include_full_description to false for wide date ranges, warns that status_category values other than 'upcoming' may fail, and advises retrying without the filter. It doesn't mention alternative tools (none exist), so the 'when not to use' aspect is absent, but the guidance is clear and actionable.

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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