1) The baseline is a null model, typically in which all of your observed variables are constrained to covary with no other variables (put another way, the covariances are fixed to 0)--just individual variances are estimated. This is what is often taken as a 'reasonable' worst-possible fitting model, against which your fitted model is compared in order to calculate relative indexes of model fit (e.g., CFI/TLI).
2) The chi-square statistic (labeled as the minimum function test statistic) is used to perform a test of perfect model fit, both for your specified and null/baseline models. It essentially is a measure of deviance between your model-implied variance/covariance matrix, and your observed variance/covariance matrix. In both cases the null of perfect fit is rejected (p < .001), though this is by design in the case of the baseline/null model. Some statisticians (e.g., Klein, 2010) argue that the chi-square test of model fit is useful in evaluating the quality of a model, but most others discourage putting a lot stock in its interpretation, both for conceptual (i.e., the null of perfect fit is unreasonable) and practical (i.e, chi-square test is sensitive to sample size) reasons (see Brown, 2015; Little, 2013, for examples). It is, however, useful for calculating a number of other, more informative, indexes of model fit.
3) Standards for what level of model fit is considered "acceptable" may differ from discipline to discipline, but at least according to Hu & Bentler (1999), you are within the realm of what is considered "acceptable". A CFI of .955 is often considered "good". Keep in mind, however, that both TLI and CFI are relative indexes of model fit--they compare the fit of your model to the fit of your (worst fitting) null model. Hu & Bentler (1999) suggested that you interpret/report both a relative and an absolute index of model fit. Absolute indexes of model fit compare the fit of your model to a perfect fitting model--RMSEA and SRMR are a couple of good candidates (the former is often calculated along with a confidence interval, which is nice).
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd Edition). New York, NY: Guilford Press.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd Edition). New York, NY: Guilford Press.
Little, T. D. (2013). Longitudinal structural equation modeling. New York, NY: Guilford Press.