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cpp-cpm-engine

by io.github.danafitkowski

xer_parser

Parse a Primavera P6 XER file and return a TABLE SUMMARY (not the full row-level data — XER row dumps explode the MCP context window).

    For each table in the XER, returns the table name, field list,
    and record count. Per-row data is intentionally omitted — for
    forensic / DCMA / windows analysis use the dedicated tools
    (``forensic_windows_analysis``, ``critical_path_validator``, etc.)
    which consume the parsed XER internally and return analytical
    summaries, not raw rows.

    Use this tool to confirm an XER is parseable, list its tables, see
    the data date / project name from PROJECT, or count activities in
    TASK before deciding which deeper tool to run.

    Args:
        xer_path:    server-side filesystem path to the XER file.
        xer_content: full text of the XER file (alternative for
            hosted/remote use). Supply EXACTLY ONE of path/content.

    Returns:
        {
          "filepath":       absolute path,
          "encoding_used":  "utf-8" | "cp1252" | ...,
          "ermhdr":         file header dict (P6 version, export user, etc.),
          "tables":         [{"name", "fields", "record_count"}, ...],
          "table_count":    int,
          "total_records":  int,
          "project_summary": {
            "proj_id", "proj_short_name", "proj_long_name",
            "data_date", "plan_end_date"
          } (from first PROJECT row, if any)
        }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xer_pathNo
xer_contentNo

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral transparency. It thoroughly discloses that the tool returns a summary, not raw rows, and explains why ('XER row dumps explode the MCP context window'). It also outlines the exact return structure, including encoding, table summaries, and project summary, providing complete insight into behavior.

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?

Although the description is longer than minimal, every sentence adds value. It is well-structured with a clear purpose statement, usage context, parameter explanations, and a detailed return format. The information is front-loaded and not repetitive, ensuring concise, purposeful prose.

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 and absence of an output schema, the description is remarkably complete. It includes a full return structure with field names and examples, explains the input alternatives, and dictates usage scenarios. This makes the tool fully self-documented and actionable 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?

The schema description coverage is 0%, so the description must fully explain parameters. It does so: 'xer_path: server-side filesystem path to the XER file. xer_content: full text of the XER file (alternative for hosted/remote use).' It also adds a critical constraint: 'Supply EXACTLY ONE of path/content.' This goes beyond schema alone and gives clear usage semantics.

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 the tool's function: 'Parse a Primavera P6 XER file and return a TABLE SUMMARY', specifying the verb 'parse' and the resource 'XER file'. It also differentiates from siblings by explicitly noting that for full row-level analysis, dedicated tools like 'forensic_windows_analysis' and 'critical_path_validator' should be used, making the scope unambiguous.

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 provides explicit when-to-use guidance: 'Use this tool to confirm an XER is parseable, list its tables, see the data date / project name from PROJECT, or count activities in TASK before deciding which deeper tool to run.' It also clearly states alternatives and exclusions, such as omitting raw row data and directing users to dedicated tools for deeper analysis.

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

A4.7/5.0
Disambiguation4/5

Each tool targets a distinct analytical deliverable (windows analysis, concurrency matrix, slip velocity, TIA, collapsed as-built, SRA, etc.), and descriptions explicitly cross-reference sibling tools to clarify boundaries. Some pairs like critical_path_validator vs dcma14_health_check and forensic_windows_analysis vs concurrent_delay_matrix share inputs and close conceptual territory, but the stated distinctions are clear enough to prevent misselection.

Naming Consistency4/5

All tool names follow a consistent lowercase snake_case style with descriptive noun phrases (e.g., forensic_windows_analysis, slip_velocity, xer_parser), so there is no mixing of conventions. However, the pattern is not verb_noun and a few names embed acronyms or numbers (dcma14_health_check, monte_carlo_p50_p80, woet_classifier), which is a minor deviation from a fully uniform naming scheme.

Tool Count5/5

13 tools is well-scoped for a forensic CPM/schedule delay analysis server. Each tool covers a distinct method or deliverable—parsing, logic health, DCMA-14, windows analysis, concurrency, slip trends, TIA, collapsed as-built, Monte Carlo SRA, maturity assessment, WOET, path tracing, and an evidence workbench—so every tool earns its place without redundancy.

Completeness5/5

The tool surface comprehensively covers the forensic delay analysis lifecycle: input parsing, schedule logic validation, DCMA-14 health assessment, retrospective windows analysis, concurrency attribution, slip trending, prospective TIA, collapsed as-built, probabilistic SRA, maturity rating, execution classification, and raw-evidence workbench. Minor gaps like schedule editing or cost analysis exist but are outside the server's stated forensic-analysis purpose.

Resources